pz = np.vectorize(ngp)
##
bz = lambda z: bz_callmodel(z, mlim)
nbar = get_shot(band, mlim)
nbar /= (4. * np.pi / 41253.) ## Sq. deg. to steradian.
##
int_frac = 0.06
nbar_wint = (1.0 + int_frac) * nbar
decband = setup[band]['decband']
colors = setup[band]['colors']
peakz = _peakz(pz)
## Hard p(z) limits.
zmin = 0.01
zmax = 10.00
## LSST whitebook p(z).
## pz = lambda z: whitebook_pz(z, ilim = 25.30)
## Change in Ckg with p(z), b(z), nbar -> p'(z), b'(z) and nbar'.
## We assume the z < 1 population of likely red galaxies has a bias of 2.04 at a mean z 0.87;
## bzz = lambda z: bz(z) if z > 1.0 else 2.04
bzz = lambda z: bz(z) if z > 3.0 else (1. + z)
## Interloper GP rewrite.
_, _, _, _, _ngp = gp_pz(band, 'Degraded', 5.5)
colors = setup[band]['colors']
spec_mlim = setup[band]['spec-maglim']
phot_mlim = setup[band]['phot-maglim']
## Spectroscopic sample p(z) and b(z).
ps = get_pz(band)
bs = lambda z: bz_callmodel(z, spec_mlim)
## Get the z percentiles for this dropout p(z).
percentiles = percentiles(ps, printit=True)
## Photometric sample p(z).
pp = get_pz(band)
peakz = _peakz(ps)
zmin = peakz - 1.0
zmax = peakz + 1.0
## Get dropout Schechter photometric sample counts for given band.
root = os.environ['LBGCMB']
data = np.loadtxt(root + '/dropouts/schechter/dat/%s' % file)
## Number of photometric galaxies per sq. deg. for given detection band limit.
ms = data[:, 0][::-1]
Npz = data[:, 1][::-1]
## Cut to half mags. above 24.0
valid = (ms % 0.5 == 0) & (ms >= 24.0) & (ms < 26.5)