def get_lf(lf, sid, bins, use_smooth_maps=False, use_binvol2 = False):
# Bin data. This is only for visualisation and to compare
# with reported binned values.
m = lf.M1450[lf.sid==sid]
#print 'Originally have {:d} quasars'.format(np.size(m))
#print 'Should have {:d} quasars'.format(np.size(m[m<-24.0]))
ms = (bins[1:]+bins[:-1])*0.5
limit = np.max(ms)
m = m[m<-24.0]
# print 'bins=', bins
#print 'limit=', limit
#print 'Have {:d} quasars'.format(np.size(m))
if use_smooth_maps:
selmaps = [x for x in smoothmaps if x.sid == sid]
else:
selmaps = [x for x in lf.maps if x.sid == sid]
v1 = np.array([totBinVol(lf, x, bins, selmaps, use_binvol2=use_binvol2) for x in m])
v1_nonzero = v1[np.where(v1>0.0)]
m = m[np.where(v1>0.0)]
h = np.histogram(m, bins=bins, weights=1.0/(v1_nonzero))
nums = h[0]
mags = (h[1][:-1] + h[1][1:])*0.5
dmags = np.diff(h[1])*0.5
left = mags - h[1][:-1]
right = h[1][1:] - mags
phi = nums
logphi = np.log10(phi) # cMpc^-3 mag^-1
# Calculate errorbars on our binned LF. These have been estimated
# using Equations 1 and 2 of Gehrels 1986 (ApJ 303 336), as
# implemented in astropy.stats.poisson_conf_interval. The
# interval='frequentist-confidence' option to that astropy function is
# exactly equal to the Gehrels formulas, although the documentation
# does not say so.
n = np.histogram(m, bins=bins)[0]
nlims = pci(n,interval='frequentist-confidence')
nlims *= phi/n
uperr = np.log10(nlims[1]) - logphi
downerr = logphi - np.log10(nlims[0])
# for a in zip(mags, n):
# print '{:.2f} {:d}'.format(a[0], a[1])
return mags, left, right, logphi, uperr, downerr
def get_lf(lf, sid, z_plot, special='None'):
# Bin data. This is only for visualisation and to compare
# with reported binned values.
m = lf.M1450[lf.sid == sid]
selmaps = [x for x in lf.maps if x.sid == sid]
if sid == 6:
# Glikman's sample needs wider bins.
bins = np.array([-26.0, -25.0, -24.0, -23.0, -22.0, -21])
elif sid == 7:
bins = np.array([-23.5, -21.5, -20.5, -19.5, -18.5])
elif sid == 10 or sid == 18:
bins = np.arange(-30.9, -17.3, 1.8)
elif special == 'croom_comparison':
# These M1450 bins result in the Mgz2 bins of Croom09. The
# 1.23 converts between the two magnitudes (Eqn B8 of Ross13).
bins = np.arange(-30, -19.5, 0.5) + 1.23
elif special == 'croom_comparison_Mgz2':
# These Mgz2 bins of Croom09.
bins = np.arange(-30, -19.5, 0.5)
else:
bins = np.arange(-30.9, -17.3, 0.6)
v1 = np.array([totBinVol(lf, x, bins, selmaps) for x in m])
v1_nonzero = v1[np.where(v1 > 0.0)]
m = m[np.where(v1 > 0.0)]
h = np.histogram(m, bins=bins, weights=1.0 / (v1_nonzero))
nums = h[0]
mags = (h[1][:-1] + h[1][1:]) * 0.5
dmags = np.diff(h[1]) * 0.5
left = mags - h[1][:-1]
right = h[1][1:] - mags
phi = nums
logphi = np.log10(phi) # cMpc^-3 mag^-1
# print 'sid=', sid
# print 'mags=', mags
# print 'nums=', nums
# print 'total=', np.sum(nums)
# Calculate errorbars on our binned LF. These have been estimated
# using Equations 1 and 2 of Gehrels 1986 (ApJ 303 336), as
# implemented in astropy.stats.poisson_conf_interval. The
# interval='frequentist-confidence' option to that astropy function is
# exactly equal to the Gehrels formulas, although the documentation
# does not say so.
n = np.histogram(m, bins=bins)[0]
nlims = pci(n, interval='frequentist-confidence')
nlims *= phi / n
uperr = np.log10(nlims[1]) - logphi
downerr = logphi - np.log10(nlims[0])
return mags, left, right, logphi, uperr, downerr
def get_lf_sample(lf, sid, z_plot):
# Bin data. This is only for visualisation and to compare
# with reported binned values.
m = lf.M1450[lf.sid == sid]
n = m.size
mlims = (m.min(), m.max())
theta = np.median(lf.samples, axis=0)
m = lfsample(theta, n, mlims)
selmaps = [x for x in lf.maps if x.sid == sid]
if sid == 6:
# Glikman's sample needs wider bins.
bins = np.array([-26.0, -25.0, -24.0, -23.0, -22.0, -21])
elif sid == 7:
bins = np.array([-23.5, -21.5, -20.5, -19.5, -18.5])
elif sid == 10:
bins = np.arange(-30.9, -17.3, 1.8)
else:
bins = np.arange(-30.9, -17.3, 0.6)
v1 = np.array([totBinVol(lf, x, bins, selmaps) for x in m])
v1_nonzero = v1[np.where(v1 > 0.0)]
m = m[np.where(v1 > 0.0)]
h = np.histogram(m, bins=bins, weights=1.0 / (v1_nonzero))
nums = h[0]
mags = (h[1][:-1] + h[1][1:]) * 0.5
dmags = np.diff(h[1]) * 0.5
left = mags - h[1][:-1]
right = h[1][1:] - mags
phi = nums
logphi = np.log10(phi) # cMpc^-3 mag^-1
# Calculate errorbars on our binned LF. These have been estimated
# using Equations 1 and 2 of Gehrels 1986 (ApJ 303 336), as
# implemented in astropy.stats.poisson_conf_interval. The
# interval='frequentist-confidence' option to that astropy function is
# exactly equal to the Gehrels formulas, although the documentation
# does not say so.
n = np.histogram(m, bins=bins)[0]
nlims = pci(n, interval='frequentist-confidence')
nlims *= phi / n
uperr = np.log10(nlims[1]) - logphi
downerr = logphi - np.log10(nlims[0])
return mags, left, right, logphi, uperr, downerr
def get_lf(zrange, bins):
z, m, p = np.loadtxt('Data/r13miz2_sample.dat',
usecols=(1, 2, 3),
unpack=True)
select = ((z >= zrange[0]) & (z < zrange[1]))
m = m[select]
p = p[select]
area = 2236.0 # deg^2
dz = 0.05
dm = 0.1
zsel, msel, psel = np.loadtxt('Data/r13miz2_selfunc.dat',
usecols=(1, 2, 3),
unpack=True)
vol = volume(zsel, area) * dz
psel[(zsel < zrange[0]) | (zsel >= zrange[1])] = 0.0
zv = np.linspace(zrange[0], zrange[1], 50)
dzv = np.diff(zv)[0]
v = volume(zv, area) * dzv
totvol = np.sum(v)
v1 = np.array(
[binvol(x, zrange, bins, msel, psel, vol, zsel, totvol) for x in m])
v1_nonzero = v1[np.where(v1 > 0.0)]
m = m[np.where(v1 > 0.0)]
h = np.histogram(m, bins=bins, weights=1.0 / (v1_nonzero))
nums = h[0]
mags = (h[1][:-1] + h[1][1:]) * 0.5
dmags = np.diff(h[1]) * 0.5
left = mags - h[1][:-1]
right = h[1][1:] - mags
phi = nums
logphi = np.log10(phi) # cMpc^-3 mag^-1
n = np.histogram(m, bins=bins)[0]
nlims = pci(n, interval='frequentist-confidence')
nlims *= phi / n
uperr = np.log10(nlims[1]) - logphi
downerr = logphi - np.log10(nlims[0])
return mags, left, right, logphi, uperr, downerr
def get_lf(lf, z_plot, sids):
sample_selection = np.in1d(lf.sid, sids)
m = lf.M1450[sample_selection]
p = lf.p[sample_selection]
sid = lf.sid[sample_selection]
m = m[p!=0.0]
p = p[p!=0.0]
sid = sid[p!=0.0]
# DR7
area = 6248.0 # deg^2
dz = 0.05
dm = 0.1
zsel, msel, psel = np.loadtxt('Data/mcgreer13_dr7selfunc.dat', usecols=(1,2,3), unpack=True)
vol = volume(zsel, area)*dz
# def volm(m, msel, psel, vsel):
# total_vol = 0.0
# bincount = 0
# bincount_pnonzero = 0
# dm = abs(msel[1]-msel[0])
# for i in xrange(msel.size):
# if (m >= msel[i]-dm) and (m < msel[i]+dm):
# bincount += 1
# if psel[i] > 0.0:
# bincount_pnonzero += 1
# # total_vol += vsel[i]*psel[i]
# total_vol += vsel[i] # Should this be vsel[i]*dm?
# return total_vol
def volm(m, msel, psel, vsel):
total_vol = 0.0
bins = np.array([-28.23, -27.73, -27.23, -26.73, -26.23, -25.73])
idx = np.searchsorted(bins, m, side='right')
mlow = bins[idx-1]
mhigh = bins[idx]
dm = abs(msel[1]-msel[0])
n = int(abs(mhigh-mlow)/dm)+1
for i in xrange(msel.size):
if (mlow >= msel[i]-dm) and (mlow < msel[i]+dm):
for j in range(n):
try:
total_vol += vsel[i+j]*psel[i+j]*dm
except(IndexError):
total_vol += 0.0
return total_vol
v1 = np.array([volm(x, msel, psel, vol) for x in m])
bins = [-28.23, -27.73, -27.23, -26.73, -26.23, -25.73]
# h = np.histogram(m,bins=bins,weights=1.0/(p*v1))
h = np.histogram(m,bins=bins,weights=1.0/v1)
print m[v1==0.0]
print p[v1==0.0]
# print volm(-26.73, msel, psel, vol, pd=True)
print '----'
# # Stripe 82
# area = 235.0 # deg^2
# dz = 0.05
# dm = 0.1
# zsel, msel, psel = np.loadtxt('Data/mcgreer13_s82selfunc.dat', usecols=(1,2,3), unpack=True)
# vol = volume(zsel, area)*dz
# mask = np.ones(len(M1450), dtype=bool)
# mask[dr7] = False
# v2 = np.array([volm(x, msel, psel, vol) for x in m[mask]])
# v = np.where(mask, v2, v1)
# bins = [-26.0, -25.0, -24.0, -23.0, -22.0, -21]
# h = np.histogram(m[1:],bins=bins,weights=1.0/(p[1:]*v[1:]))
nums = h[0]
mags = (h[1][:-1] + h[1][1:])*0.5
dmags = np.diff(h[1])*0.5
left = mags - h[1][:-1]
right = h[1][1:] - mags
phi = nums/np.diff(h[1])
logphi = np.log10(phi) # cMpc^-3 mag^-1
# Calculate errorbars on our binned LF. These have been estimated
# using Equations 1 and 2 of Gehrels 1986 (ApJ 303 336), as
# implemented in astropy.stats.poisson_conf_interval. The
# interval='frequentist-confidence' option to that astropy function is
# exactly equal to the Gehrels formulas, although the documentation
# does not say so.
n = np.histogram(m,bins=bins)[0]
print n
nlims = pci(n,interval='frequentist-confidence')
nlims *= phi/n
uperr = np.log10(nlims[1]) - logphi
downerr = logphi - np.log10(nlims[0])
print mags
print logphi
return mags, left, right, logphi, uperr, downerr
def get_lf(zrange, bins, old=True):
if old:
z, m, p = np.loadtxt('Data/glikman11qso.dat',
usecols=(1, 2, 3),
unpack=True)
else:
z, m, p = np.loadtxt('Data/glikman11debug.dat',
usecols=(1, 2, 3),
unpack=True)
select = ((z >= zrange[0]) & (z < zrange[1]))
m = m[select]
p = p[select]
area = 1.71 # deg^2
dz = 0.02
dm = 0.05
if old:
zsel, msel, psel = np.loadtxt('Data/glikman11_selfunc_ndwfs_old.dat',
usecols=(1, 2, 3),
unpack=True)
else:
zsel, msel, psel = np.loadtxt('Data/glikman11_selfunc_ndwfs.dat',
usecols=(1, 2, 3),
unpack=True)
vol = volume(zsel, area) * dz
psel[(zsel < zrange[0]) | (zsel >= zrange[1])] = 0.0
# m[:12] because only those qsos are from NDWFS
v1 = np.array(
[binvol(x, zrange, bins, msel, psel, vol, zsel) for x in m[:12]])
area = 2.05 # deg^2
if old:
zsel, msel, psel = np.loadtxt('Data/glikman11_selfunc_dls_old.dat',
usecols=(1, 2, 3),
unpack=True)
else:
zsel, msel, psel = np.loadtxt('Data/glikman11_selfunc_dls.dat',
usecols=(1, 2, 3),
unpack=True)
vol = volume(zsel, area) * dz
psel[(zsel < zrange[0]) | (zsel >= zrange[1])] = 0.0
# m[12:] because only those qsos are from DLS
v2 = np.array(
[binvol(x, zrange, bins, msel, psel, vol, zsel) for x in m[12:]])
v = np.concatenate((v1, v2))
print v.size
v_nonzero = v[np.where(v > 0.0)]
print v_nonzero.size
m = m[np.where(v > 0.0)]
h = np.histogram(m, bins=bins, weights=1.0 / (v_nonzero))
nums = h[0]
mags = (h[1][:-1] + h[1][1:]) * 0.5
dmags = np.diff(h[1]) * 0.5
left = mags - h[1][:-1]
right = h[1][1:] - mags
phi = nums
logphi = np.log10(phi) # cMpc^-3 mag^-1
n = np.histogram(m, bins=bins)[0]
print n
nlims = pci(n, interval='frequentist-confidence')
nlims *= phi / n
uperr = np.log10(nlims[1]) - logphi
downerr = logphi - np.log10(nlims[0])
mags, b, c = bs(m, m, bins=bins)
left = mags - b[:-1]
right = b[1:] - mags
return mags, left, right, logphi, uperr, downerr
def rhoqso3(lfs, mag_threshold, bins, **kwargs):
zs = []
rhos = []
uerr = []
lerr = []
for x in lfs:
z = (x.zlims[0]+x.zlims[1])/2
sids = np.unique(x.sid)
logphis = []
for sid in sids:
mags, left, right, logphi, uperr, downerr = get_lf(x, sid, bins, **kwargs)
logphis.append(10.0**logphi)
Nbins = np.size(mags)
Nsamples = len(logphis)
print('Nbins=', Nbins)
print('Nsamples=', Nsamples)
total_phi = np.zeros(Nbins)
for i in range(Nbins):
vals = []
nonzero_counter = 0
for j in range(Nsamples):
if logphis[j][i] > 0.0:
nonzero_counter += 1
vals.append(logphis[j][i])
if nonzero_counter > 0:
total_phi[i] = np.mean(np.array(vals))
# print i, total_phi[i]
# N = len(logphis)
# if N > 2:
# print 'We have a problem: N > 2!!!'
# if N > 1:
# total_phi = logphis[0]
# for ns in range(1,N):
# for i, p in enumerate(logphis[ns]):
# if total_phi[i] == 0.0:
# total_phi[i] = total_phi[i] + p
# elif p > 0.0:
# total_phi[i] = (total_phi[i] + p)/2
# logphi = total_phi
# else:
# logphi = np.sum(logphis, axis=0)
logphi = total_phi
#row1 = logphis[0]
#row2 = logphis[1]
# logphi = np.sum(logphis, axis=0)
# #logphi = total_phi
# logphi_test = np.mean(logphis, axis=0)
#for phivalue in zip(row1, row2, logphi):
# print '{:.2e} {:.2e} {:.2e}'.format(*phivalue)
#print logphi
dm = left + right
phidm = logphi * dm
intphidm = np.cumsum(phidm)
# for omega in zip(mags, logphi):
# print '{:.2f} {:.2e}'.format(omega[0], omega[1])
# print 'mags=', mags
# print 'logphi=', logphi
#
assert(np.all(np.diff(mags) > 0))
# rho = np.interp(mag_threshold, mags, intphidm)
rho = intphidm[-1]
nqso = np.size(x.M1450[x.M1450 < mag_threshold])
if nqso > 0:
nlims = pci(nqso,interval='frequentist-confidence')
nlims *= rho/nqso
uperr = nlims[1] - rho
downerr = rho - nlims[0]
else:
uperr = 0.0
downerr = 0.0
if np.max(mags[logphi > 0.0]) < mag_threshold:
# not reaching threshold
rho = 0.0
uperr = 0.0
downerr = 0.0
rhos.append(rho)
zs.append(z)
uerr.append(uperr)
lerr.append(downerr)
return zs, rhos, uerr, lerr
def get_lf(lf, z_plot, sids):
sample_selection = np.in1d(lf.sid, sids)
m = lf.M1450[sample_selection]
p = lf.p[sample_selection]
sid = lf.sid[sample_selection]
print m
print np.sort(m)
m = m[p!=0.0]
p = p[p!=0.0]
sid = sid[p!=0.0]
# Jiang 2008
area = 260.0 # deg^2
dz = 0.025
dm = 0.1
zsel, msel, psel = np.loadtxt('Data/jiang08_sel.dat', usecols=(1,2,3), unpack=True)
vol = volume(zsel, area)*dz
# def volm(mag, magsel, probsel, volsel, zsel):
# # Below, np.isclose() selects the tile to which this magnitude
# # belongs at each redshift. We then select the tiles for
# # which selection probability is non-zero (probsel[t]>0.0) and
# # sum the volume of all those tiles.
# t = np.isclose(magsel, mag, rtol=0.0, atol=dm*0.5)
# rs = zsel[t][probsel[t]>0.0]
# vl = volsel[t][probsel[t]>0.0]
# return np.trapz(vl, rs)
# # return np.sum(volsel[t][probsel[t]>0.0], dtype=np.float64)
# def volm(mag, magsel, probsel, volsel, zsel):
# # Below, np.isclose() selects the tile to which this magnitude
# # belongs at each redshift. We then select the tiles for
# # which selection probability is non-zero (probsel[t]>0.0) and
# # sum the volume of all those tiles.
# t = np.isclose(magsel, mag, rtol=0.0, atol=dm*0.5)
# return np.sum(volsel[t][probsel[t]>0.0], dtype=np.float64)
# def volm(mag, magsel, probsel, volsel, zsel):
# # Below, np.isclose() selects the tile to which this magnitude
# # belongs at each redshift. We then select the tiles for
# # which selection probability is non-zero (probsel[t]>0.0) and
# # sum the volume of all those tiles.
# t = np.isclose(magsel, mag, rtol=0.0, atol=dm*0.5)
# return np.sum(volsel[t]*probsel[t], dtype=np.float64)
bins = [-26.25, -25.35, -24.85]
def volm(mag, magsel, probsel, volsel, zsel):
# Below, np.isclose() selects the tile to which this magnitude
# belongs at each redshift. We then select the tiles for
# which selection probability is non-zero (probsel[t]>0.0) and
# sum the volume of all those tiles.
bn = np.searchsorted(bins, mag)
t = ((magsel < bins[1]) & (mag >= bins[0]))
return np.sum(volsel[t]*probsel[t], dtype=np.float64)
m1 = np.concatenate((m[:2],m[3:6]))
v1 = np.array([volm(x, msel, psel, vol, zsel) for x in m1])
print 'v1=', v1
# Jiang 2009
area = 195.0 # deg^2
dz = 0.025
dm = 0.1
zsel, msel, psel = np.loadtxt('Data/jiang09_sel.dat', usecols=(1,2,3), unpack=True)
vol = volume(zsel, area)*dz
v2 = np.array([volm(x, msel, psel, vol, zsel) for x in m[6:]])
v = np.concatenate((v1, v2))
bins = [-26.25, -25.35, -24.85]
# h = np.histogram(m,bins=bins,weights=1.0/(p*v))
# h = np.histogram(np.concatenate((m[:2],m[3:])),bins=bins,weights=1.0/(np.concatenate((p[:2],p[3:]))*v))
h = np.histogram(np.concatenate((m[:2],m[3:])),bins=bins,weights=1.0/v)
nums = h[0]
mags = (h[1][:-1] + h[1][1:])*0.5
dmags = np.diff(h[1])*0.5
left = mags - h[1][:-1]
right = h[1][1:] - mags
phi = nums/np.diff(h[1])
logphi = np.log10(phi) # cMpc^-3 mag^-1
# Calculate errorbars on our binned LF. These have been estimated
# using Equations 1 and 2 of Gehrels 1986 (ApJ 303 336), as
# implemented in astropy.stats.poisson_conf_interval. The
# interval='frequentist-confidence' option to that astropy function is
# exactly equal to the Gehrels formulas, although the documentation
# does not say so.
n = np.histogram(m,bins=bins)[0]
nlims = pci(n,interval='frequentist-confidence')
nlims *= phi/n
uperr = np.log10(nlims[1]) - logphi
downerr = logphi - np.log10(nlims[0])
# mags, b, c = bs(m, m, bins=bins)
# left = mags - b[:-1]
# right = b[1:] - mags
print mags
print phi
return mags, left, right, logphi, uperr, downerr
