def calib_jungfrau(det, evt, cmpars=(7,3,200,10), **kwa):
    """
    Returns calibrated jungfrau data

    - gets constants
    - gets raw data
    - evaluates (code - pedestal - offset)
    - applys common mode correction if turned on
    - apply gain factor

    Parameters

    - det (psana.Detector) - Detector object
    - evt (psana.Event)    - Event object
    - cmpars (tuple) - common mode parameters
        - cmpars[0] - algorithm # 7-for jungfrau
        - cmpars[1] - control bit-word 1-in rows, 2-in columns
        - cmpars[2] - maximal applied correction
    - **kwa - used here and passed to det.mask_v2 or det.mask_comb
      - nda_raw - if not None, substitutes evt.raw()
      - mbits - DEPRECATED parameter of the det.mask_comb(...)
      - mask - user defined mask passed as optional parameter
    """

    t00 = time()

    src = det.source # - src (psana.Source)   - Source object

    nda_raw = kwa.get('nda_raw', None)
    arr = det.raw(evt) if nda_raw is None else nda_raw # shape:(<npanels>, 512, 1024) dtype:uint16
    if arr is None: return None

    t01 = time()

    peds = det.pedestals(evt) # - 4d pedestals shape:(3, 1, 512, 1024) dtype:float32
    if peds is None: return None

    t02 = time()

    gain = det.gain(evt)      # - 4d gains
    offs = det.offset(evt)    # - 4d offset

    t03 = time()

    detname = string_from_source(det.source)
    cmp = det.common_mode(evt) if cmpars is None else cmpars

    t04 = time()

    if gain is None: gain = np.ones_like(peds)  # - 4d gains
    if offs is None: offs = np.zeros_like(peds) # - 4d gains

    # cache
    gfac = store.gfac.get(detname, None) # det.name
    if gfac is None:
       gfac = divide_protected(np.ones_like(peds), gain)
       store.gfac[detname] = gfac
       store.arr1 = np.ones_like(arr, dtype=np.int8)

    t05 = time()

    # Define bool arrays of ranges
    # faster than bit operations
    gr0 = arr <  BW1              # 490 us
    gr1 =(arr >= BW1) & (arr<BW2) # 714 us
    gr2 = arr >= BW3              # 400 us

    t06 = time()

    # Subtract pedestals
    arrf = np.array(arr & MSK, dtype=np.float32)

    t07 = time()

    arrf[gr0] -= peds[0,gr0]
    arrf[gr1] -= peds[1,gr1] #- arrf[gr1]
    arrf[gr2] -= peds[2,gr2] #- arrf[gr2]

    t08 = time()

    factor = np.select((gr0, gr1, gr2), (gfac[0,:], gfac[1,:], gfac[2,:]), default=1) # 2msec

    t09 = time()

    offset = np.select((gr0, gr1, gr2), (offs[0,:], offs[1,:], offs[2,:]), default=0)

    t10 = time()

    arrf -= offset # Apply offset correction

    t11 = time()

    if store.mask is None:
       store.mask = det.mask_total(evt, **kwa)
    mask = store.mask

    t12 = time()

    if cmp is not None:
      mode, cormax = int(cmp[1]), cmp[2]
      npixmin = cmp[3] if len(cmp)>3 else 10
      if mode>0:
        #arr1 = store.arr1
        #grhg = np.select((gr0,  gr1), (arr1, arr1), default=0)
        logger.debug(info_ndarr(gr0, 'gain group0'))
        logger.debug(info_ndarr(mask, 'mask'))
        t0_sec_cm = time()
        gmask = np.bitwise_and(gr0, mask) if mask is not None else gr0
        #sh = (nsegs, 512, 1024)
        hrows = 256 #512/2
        for s in range(arrf.shape[0]):
          if mode & 4: # in banks: (512/2,1024/16) = (256,64) pixels # 100 ms
            common_mode_2d_hsplit_nbanks(arrf[s,:hrows,:], mask=gmask[s,:hrows,:], nbanks=16, cormax=cormax, npix_min=npixmin)
            common_mode_2d_hsplit_nbanks(arrf[s,hrows:,:], mask=gmask[s,hrows:,:], nbanks=16, cormax=cormax, npix_min=npixmin)

          if mode & 1: # in rows per bank: 1024/16 = 64 pixels # 275 ms
            common_mode_rows_hsplit_nbanks(arrf[s,], mask=gmask[s,], nbanks=16, cormax=cormax, npix_min=npixmin)

          if mode & 2: # in cols per bank: 512/2 = 256 pixels  # 290 ms
            common_mode_cols(arrf[s,:hrows,:], mask=gmask[s,:hrows,:], cormax=cormax, npix_min=npixmin)
            common_mode_cols(arrf[s,hrows:,:], mask=gmask[s,hrows:,:], cormax=cormax, npix_min=npixmin)

        logger.debug('TIME: common-mode correction time = %.6f sec' % (time()-t0_sec_cm))

    t13 = time()

    resp = arrf * factor if mask is None else arrf * factor * mask # gain correction

    t14 = time()
    times = np.array((t00, t01, t02, t03, t04, t05, t06, t07, t08, t09, t10, t11, t12, t13, t14), dtype=np.float64)

    return resp, times