def work(z):
Lband, M_AB, S_nu, Phi = _qlf.qlf(band, z, Lbolbins)
Phi /= (U.MPC ** 3)
Dc = cosmology.Dc(z)
DL = Dc * (1 + z)
distance_modulus = 5 * numpy.log10(DL / (0.01 * U.KPC))
m_AB = M_AB + distance_modulus
spl = UnivariateSpline(-m_AB, Phi, k=5)
print z, DL, m_AB[0], m_AB[-1], Phi.sum(), m_AB.max(), m_AB.min()
integrated = spl.integral(-faint, -bright)
if integrated < 0: integrated = 0
return integrated
def qlf(cosmology, band, magnitude=False, returnraw=False):
""" returns an scipy interpolated function for the hopkins 2007 QLF,
at a given band.
band can be a frequency, or 'bol', 'blue', 'ir', 'soft', 'hard'.
HOPKINS2007 cosmology is implied.
result shall not depend on the input cosmology but the HOPKINS cosmology
is implied in the fits.
if return raw is true, return
zrange, Lbol, Lband, M_AB, S_nu, Phi
"""
zbins = numpy.linspace(0, 6, 200)
Lbolbins=numpy.linspace(8, 18, 300)
U = cosmology.units
banddict = {'bol':0, 'blue':-1, 'ir':-2, 'soft':-3, 'hard': -4}
from scipy.interpolate import RectBivariateSpline
if band in banddict:
band = banddict[band]
else: band = _bandconv(U, band, hertz=True)
key = band
if key not in _qlf_interp:
v = numpy.empty(numpy.broadcast(Lbolbins[None, :], zbins[:, None]).shape)
Lband, M_AB, S_nu, Phi = _qlf.qlf(band, 1.0, Lbolbins)
with sharedmem.TPool() as pool:
def work(v, z):
Lband_, M_AB_, S_nu, Phi = _qlf.qlf(band, z, Lbolbins)
v[:] = Phi
pool.starmap(work, zip(v, zbins))
v /= (U.MPC ** 3)
# Notice that hopkins used 3.9e33 ergs/s for Lsun, but we use a different number.
# but the internal fits of his numbers
# thus we skip the conversion, in order to match the fits with luminosity in terms of Lsun.
# Lbol = Lbol - numpy.log10(U.SOLARLUMINOSITY/U.ERG*U.SECOND) + numpy.log10(3.9e33)
data = numpy.empty(shape=len(Lbolbins),
dtype=[
('Lbol', 'f4'),
('Lband', 'f4'),
('M_AB', 'f4'),
('S_nu', 'f4'),
('Phi', ('f4', v.shape[0]))])
data['Lbol'] = Lbolbins
data['Lband'] = Lband
data['M_AB'] = M_AB
data['S_nu'] = S_nu
data['Phi'] = v.T
_qlf_interp[key] = data
data = _qlf_interp[key]
if returnraw:
return data.view(numpy.recarray)
if magnitude:
func = RectBivariateSpline(zbins, - data['M_AB'], data['Phi'].T)
func.x = zbins
func.y = -data['M_AB']
func.z = data['Phi'].T
return func
else:
func = RectBivariateSpline(zbins, data['Lband'], data['Phi'].T)
func.x = zbins
func.y = data['Lband']
func.z = data['Phi'].T
return func
def work(v, z): Lband_, M_AB_, S_nu, Phi = _qlf.qlf(band, z, Lbolbins) v[:] = Phi
