# like This paper computed IGM transmission values using
# IGMtransmissiona (Harrison, Meiksin & Stock 2011), based on the
# transmission curves of Meiksin (2006). aAvailable for download from
# http://code.google.com/p/igmtransmission If the LLS distribution
# from Inoue & Iwata (2008) is used, a reference to their work should
# be added as well.
from barak.io import readtxt
import numpy as np
# z=2.76, Inoue LLS code. Diffuse IGM normalisation 0.07553
T = readtxt("averageTransmission.dat", names="wa,tr")
WA = np.arange(3180, 8000, 1.0)
TR = np.interp(WA, T.wa, T.tr)
TR[WA > 4543.0] = 1
from barak.sed import get_bands
u, g = get_bands("sdss", ["u", "g"])
utr = np.interp(WA, u.wa, u.tr)
av_utr = (utr * TR).sum() / utr.sum()
print "u bandpass-weighted transmission", av_utr
print "mag extinct", -2.5 * np.log10(av_utr)
gtr = np.interp(WA, g.wa, g.tr)
av_gtr = (gtr * TR).sum() / gtr.sum()
print "g bandpass-weighted transmission", av_gtr
print "mag extinct", -2.5 * np.log10(av_gtr)
from barak.sed import get_bands, get_SEDs
import pylab as pl
import numpy as np
fors_u, fors_g, fors_r = get_bands('FORS','u,g,r',ccd='blue')
sdss_u, sdss_g, sdss_r = get_bands('SDSS','u,g,r')
pl.figure()
for b in fors_u, fors_g, fors_r, sdss_u, sdss_g, sdss_r:
b.plot()
pickles = get_SEDs('pickles')
fig = pl.figure()
fig.subplots_adjust(left=0.18)
for p in pickles:
p.plot(log=1)
pl.title('Pickles stellar library')
p_umg = [p.calc_colour(sdss_u, sdss_g, 'AB') for p in pickles]
p_gmr = [p.calc_colour(sdss_g, sdss_r, 'AB') for p in pickles]
tLBG = get_SEDs('LBG', 'lbg_em.dat')
tLBGa = get_SEDs('LBG', 'lbg_abs.dat')
tLBG_umg, tLBG_gmr = [], []
tLBGa_umg, tLBGa_gmr = [], []
zlabels = []
for z in np.arange(2.2, 3.7, 0.2):
zlabels.append(str(z))
tLBG.redshift_to(z)
tLBGa.redshift_to(z)
tLBG_umg.append(tLBG.calc_colour(sdss_u,sdss_g, 'AB'))
tLBG_gmr.append(tLBG.calc_colour(sdss_g,sdss_r, 'AB'))
# like This paper computed IGM transmission values using
# IGMtransmissiona (Harrison, Meiksin & Stock 2011), based on the
# transmission curves of Meiksin (2006). aAvailable for download from
# http://code.google.com/p/igmtransmission If the LLS distribution
# from Inoue & Iwata (2008) is used, a reference to their work should
# be added as well.
from barak.io import readtxt
import numpy as np
# z=2.76, Inoue LLS code. Diffuse IGM normalisation 0.07553
T = readtxt('averageTransmission.dat', names='wa,tr')
WA = np.arange(3180, 8000, 1.)
TR = np.interp(WA, T.wa, T.tr)
TR[WA > 4543.] = 1
from barak.sed import get_bands
u, g = get_bands('sdss', ['u', 'g'])
utr = np.interp(WA, u.wa, u.tr)
av_utr = (utr * TR).sum() / utr.sum()
print 'u bandpass-weighted transmission', av_utr
print 'mag extinct', -2.5 * np.log10(av_utr)
gtr = np.interp(WA, g.wa, g.tr)
av_gtr = (gtr * TR).sum() / gtr.sum()
print 'g bandpass-weighted transmission', av_gtr
print 'mag extinct', -2.5 * np.log10(av_gtr)
