def stack(data, delta=False):
nstack = int((forest.lmax - forest.lmin) / forest.dll) + 1
ll = forest.lmin + sp.arange(nstack) * forest.dll
st = sp.zeros(nstack)
wst = sp.zeros(nstack)
for p in sorted(list(data.keys())):
for d in data[p]:
if delta:
de = d.de
we = d.we
else:
de = d.fl / d.co
var_lss = forest.var_lss(d.ll)
eta = forest.eta(d.ll)
fudge = forest.fudge(d.ll)
var = 1. / d.iv / d.co**2
we = 1. / variance(var, eta, var_lss, fudge)
bins = ((d.ll - forest.lmin) / forest.dll + 0.5).astype(int)
c = sp.bincount(bins, weights=de * we)
st[:len(c)] += c
c = sp.bincount(bins, weights=we)
wst[:len(c)] += c
w = wst > 0
st[w] /= wst[w]
return ll, st, wst
def stack_flux(data, delta):
'''Make a weighted sum of flux/delta values in wavelength bins.'''
nstack = int((forest.lmax - forest.lmin) / forest.dll) + 1
ll = forest.lmin + sp.arange(nstack) * forest.dll
st = sp.zeros(nstack)
wst = sp.zeros(nstack)
data_bad_cont = []
# Stack flux & weights, or deltas & weights
for d in data:
if d.bad_cont is not None:
data_bad_cont.append(d)
continue
bins=((d.ll - d.lmin) / d.dll + 0.5).astype(int)
eta = forest.eta(d.ll)
var_lss = forest.var_lss(d.ll)
fudge = forest.fudge(d.ll)
if (delta == 0):
# convert ivar into normalized ivar (going from flux units to F units)
ivar_F = d.iv * d.co**2
# correct this variance, adding the var_lss and eta factors
var_F = 1./ivar_F
var_F_tot = var_F*eta + var_lss + fudge/var_F
# convert back to flux units
var_flux_tot = var_F_tot * d.co**2
we = 1./var_flux_tot
c = sp.bincount(bins, weights = d.fl * we)
else:
iv = d.iv / eta
we = iv * d.co**2 / (iv * d.co**2 * var_lss + 1)
c = sp.bincount(bins, weights = (d.fl/d.co - 1) * we)
st[:len(c)] += c
c = sp.bincount(bins, weights = we)
wst[:len(c)] += c
w = wst>0
st[w] /= wst[w]
for d in data_bad_cont:
print ("rejected {} due to {}\n".format(d.thid,d.bad_cont))
return ll, st, wst
def mc(data):
nmc = int((forest.lmax_rest - forest.lmin_rest) / forest.dll) + 1
mcont = sp.zeros(nmc)
wcont = sp.zeros(nmc)
ll = forest.lmin_rest + (sp.arange(nmc) + .5) * (forest.lmax_rest -
forest.lmin_rest) / nmc
for p in sorted(list(data.keys())):
for d in data[p]:
bins = ((d.ll - forest.lmin_rest - sp.log10(1 + d.zqso)) /
(forest.lmax_rest - forest.lmin_rest) * nmc).astype(int)
var_lss = forest.var_lss(d.ll)
eta = forest.eta(d.ll)
fudge = forest.fudge(d.ll)
var = 1. / d.iv / d.co**2
we = 1 / variance(var, eta, var_lss, fudge)
c = sp.bincount(bins, weights=d.fl / d.co * we)
mcont[:len(c)] += c
c = sp.bincount(bins, weights=we)
wcont[:len(c)] += c
w = wcont > 0
mcont[w] /= wcont[w]
mcont /= mcont.mean()
return ll, mcont, wcont
# Fit the continuum for the stack
g = data[0]
d = forest(ll1, st1, wst1, g.thid, g.ra, g.dec, g.zqso, g.plate, g.mjd,
g.fid, g.order)
try:
d.cont_fit()
except:
print 'Error fitting continuum: ' + str(g.thid)
break
# Create deltas and weights in same bin widths as before
nstack = int((forest.lmax - forest.lmin) / forest.dll) + 1
de1 = sp.zeros(nstack)
bins = ((d.ll - d.lmin) / d.dll + 0.5).astype(int)
c = sp.bincount(bins, weights=d.fl / d.co - 1)
de1[:len(c)] += c
eta = forest.eta(d.ll)
var_lss = forest.var_lss(d.ll)
iv = d.iv / eta
we = iv * d.co**2 / (iv * d.co**2 * var_lss + 1)
c = sp.bincount(bins, weights=we)
wst1[:len(c)] += c
# Get rid of leading and trailing zeros
w = (wst1 != 0.)
we1 = wst1[w]
wei1.append(we1)
w = (wst2 != 0.)
we2 = wst2[w]
wei2.append(we2)
# Get rid of leading and trailing zeros