def mass(z, zo):
s = c * stattools.Sbi(z) / (1+zo)
# using z=0 as the cosmological redshift
if '--z=0' in sys.argv:
zo = 0
m = scalings.sigma(s, zo)
r = conversions.rsph(m, zo, ref='200c', unit='kpc')
return s, m, r
def run(center, min_Nperbin=15, min_binsize=250,
maingap=500, maxgap=1000, sigma=False, version='1'):
#center = centers[i]
id = center[0]
xo = center[1]
yo = center[2]
zo = center[3]
j = close(xo, yo, zo, ra, dec, z)
r = astCoords.calcAngSepDeg(xo, yo, ra[j], dec[j])
r = scipy.array([cosmology.dProj(zo, ri, input_unit='deg', unit='Mpc') \
for ri in r])
if '2' in version:
r *= (1 + zo)
r *= 1e3
outplot = get_plotname(version, id)
#output = 'phase%s/plots/v%s/rv/halo-%d.png' %(phase, version, id)
m = members.sgapper(r, z[j], zo=zo, min_Nperbin=min_Nperbin,
maingap=maingap, maxgap=maxgap, sigma=sigma,
verbose=False, debug=False, plot_output=outplot,
full_output=True)
m, binsize, nit, incut = m
Nm = len(m)
zo = stattools.Cbi(z[j[m]])
s, m200, r200 = mass(z[j[m]], zo)
mo = m[r[m] <= r200]
n200 = -1
it = []
while n200 != len(mo):
try:
n200 = len(mo)
s, m200, r200 = mass(z[j[mo]], zo)
#zo = stattools.Cbi(z[j[mo]])
mo = m[r[m] <= r200]
# should maybe find a more sophisticated solution by, say, averaging
# all items from one of these "cycles"
if r200 in it:
break
it.append(r200)
except ZeroDivisionError:
print 'broke'
break
if n200 > 15:
stats = (stattools.Cbi, stattools.Sbi)
z_err, s_err = stattools.bootstrap(stats, z[j[incut]],
n_obj=Nm, n_samples=1000)
s_err = c * s_err / (1+zo)
else:
z_err = scipy.std(z) / scipy.sqrt(Nm)
s_err = c/(1+zo) * z_err / scipy.sqrt(2)
m200, m200_err = scalings.sigma(s, zo, s_err, z_err,
separate_errors=True)
r200 = conversions.rsph(m200, zo)
# to make it their format
m200_err = [scipy.log10(1+me/m200) for me in m200_err]
try:
#if n200 >
line = '%4d %.3f %.3f %6d %.1e %4d %.1f %4d %.2f %.2f %4d' \
%(id, xo*deg2rad, yo*deg2rad, round(c*zo, 0), m200,
round(s, 0), r200/1e3, n200, m200_err[0], m200_err[1], Nm)
except TypeError:
line = '%4d %.3f %.3f %6d -1 -1 -1 -1 -1 -1 %4d' \
%(id, xo*deg2rad, yo*deg2rad, round(c*zo, 0), Nm)
print line, '%4.1f' %max(r[m]/1e3)
out = open(output, 'a')
print >>out, line
out.close()
write_members(id, galid[j[m]], galid[j[mo]])
return m200
def M200(r, z, ntot=0, errors=False, converge=True, verbose=False):
"""
ntot is to be used in the bootstrap resampling
"""
c = 299792.458
def _mass(z, zo):
s = c * stattools.Sbi(z) / (1 + zo)
m = scalings.sigma(s, zo)
r200 = conversions.rsph(m, zo, ref='200c', unit='kpc')
return s, m, r200
zo = stattools.Cbi(z)
s, m, r200 = _mass(z, zo)
if verbose:
print 'iteration 0:'
print ' sigma = %d km/s' %int(s)
print ' m200 = %.1e' %m
print ' r200 = %d' %int(r200)
print ' Ngal = %d' %len(r)
j = scipy.arange(len(r), dtype=int)
if converge:
jo = j[r < r200]
n200 = 0
it = []
while len(jo) != n200:
try:
n200 = len(jo)
s, m, r200 = _mass(z[jo], zo)
if verbose:
print 'iteration %d:' %(len(it)+1)
try:
print ' sigma = %d km/s' %int(s)
print ' m200 = %.1e' %m
print ' r200 = %d' %int(r200)
print ' n200 = %d' %n200
except ValueError:
print ' Measurement failed'
jo = j[r < r200]
if r200 in it:
break
it.append(r200)
except ZeroDivisionError:
break
else:
n200 = len(r)
if n200 == 0:
return zo, -1, (-1, (-1,-1)), -1, -1
if errors:
if n200 > 15:
stats = (stattools.Cbi, stattools.Sbi)
z_err, s_err = stattools.bootstrap(stats, z, ntot)
s_err *= c / (1 + zo)
else:
z_err = scipy.std(z) / scipy.sqrt(n200)
s_err = c/(1+zo) * z_err / scipy.sqrt(2)
m = scalings.sigma(s, zo, ds=s_err, dz=z_err,
scaling='evrard08', separate_errors=True)
r = conversions.rsph(m[0], zo, ref='200c', unit='kpc')
merr = [scipy.log10(1+me/m[0]) for me in m[1]]
m = (m[0], merr)
else:
m = scalings.sigma(s, zo)
r = conversions.rsph(m, zo, ref='200c', unit='kpc')
return zo, s, m, r, n200
def _mass(z, zo): s = c * stattools.Sbi(z) / (1 + zo) m = scalings.sigma(s, zo) r200 = conversions.rsph(m, zo, ref='200c', unit='kpc') return s, m, r200
def run(catalog='HOD2_SW'):
print catalog
halofile = os.path.join('Phase_2_input_catalogues',
'{0}_input_halo_data_M200c.txt'.format(catalog))
truehalos = readfile.dict(halofile)
truehalos['logm200'] = truehalos.pop('m200')
truehalos['m200'] = 10**truehalos['logm200']
truehalos['z'] = truehalos['v_res'] / c
truehalos['m200hz'] = cosmology.E(truehalos['z']) * truehalos['m200']
Nhalo = len(truehalos['halo-id'])
galfile = os.path.join('Phase_2_input_catalogues',
'{0}_input_galaxy_data.txt'.format(catalog))
truegals = readfile.dict(galfile)
Ngals = len(truegals)
s = [
stattools.Sbi(truegals['v_res'][truegals['halo-id'] == i])
for i in xrange(1, Nhalo + 1)
]
truehalos['sigma'] = scipy.array(s) / (1 + truehalos['z'])
# write this out
output = '{0}_input_halo_data_M200c_sigma.txt'.format(catalog)
out = open(output, 'w')
print >> out, '# halo-id m200 ra dec z vdisp'
for i in xrange(Nhalo):
print >>out, '%d %.3e %.9f %.9f %.6f %7.2f' \
%(truehalos['halo-id'][i], truehalos['m200'][i],
truehalos['ra'][i], truehalos['dec'][i],
truehalos['z'][i], truehalos['sigma'][i])
out.close()
print 'Saved to', output
# fit scaling relation
j = scipy.arange(Nhalo)
#j = j[truehalos['m200hz'] >= 1e14]
Mo = int(round(scipy.median(truehalos['m200'][j]), 0))
So = int(round(scipy.median(truehalos['sigma'][j]), 0))
s = scipy.logspace(2, 3.3, 100)
t = scipy.logspace(13, 15.5, 100)
#Amle, Bmle, Smle = lnr.mle(truehalos['sigma']/So, truehalos['m200'],
#logify=True)
Amle, Bmle, Smle = lnr.mle(truehalos['m200hz'][j] / Mo,
truehalos['sigma'][j],
logify=True)
print Amle, Bmle, Smle
#A, B, Sint = lnr.mcmc(truehalos['sigma']/So, truehalos['m200'],
#nsteps=2000, logify=True, output=(50,16,84))
A, B, Sint = lnr.mcmc(truehalos['m200hz'][j] / Mo,
truehalos['sigma'][j],
nsteps=2000,
logify=True,
output=(50, 16, 84))
print A
print B
print Sint
#pylab.plot(truehalos['sigma'], truehalos['m200'], 'k.')
pylab.plot(truehalos['m200hz'], truehalos['sigma'], 'k.')
if len(j) < Nhalo:
pylab.plot(truehalos['m200hz'][j], truehalos['sigma'][j], 'b.')
#bestfit = '$S_0=%d$\n$A=%.3f$\n$B=%.3f$' %(So, A, B)
#bestfit = r'$S_0=%d\,\mathrm{km\,s^{-1}}$' %(So)
#bestfit += '\n'
#bestfit += r'$A=%.3f_{-%.4f}^{+%.4f}$' %(A[0], A[0]-A[1], A[2]-A[0])
#bestfit += '\n'
bestfit = r'$B=%.3f_{-%.3f}^{+%.3f}$' % (B[0], B[0] - B[1], B[2] - B[0])
pylab.plot(t, 10**A[0] * (t / Mo)**B[0], 'r-', lw=2) #, label=bestfit)
#Slabel = r'$\sigma_{M|S}=%.3f_{-%.3f}^{+%.3f}$' \
#%(Sint[0], Sint[0]-Sint[1], Sint[2]-Sint[0])
Slabel = r'$\sigma_{S|M}=%.3f_{-%.3f}^{+%.3f}$' \
%(Sint[0], Sint[0]-Sint[1], Sint[2]-Sint[0])
#pylab.plot(t, (1-Sint[0]) * 10**A[0] * (t/Mo)**B[0], 'r--')
#pylab.plot(t, (1+Sint[0]) * 10**A[0] * (t/Mo)**B[0], 'r--')#,
#label=Slabel)
pylab.plot(scalings.sigma(s, 0, scaling='evrard08'),
s,
'c-',
lw=2,
label='Evrard+08')
pylab.plot(scalings.sigma(s, 0, scaling='munari13dm'),
s,
'-',
color='orange',
lw=2,
label='Munari+13 DM')
pylab.plot(scalings.sigma(s, 0, scaling='munari13sub'),
s,
'--',
color='orange',
lw=2,
label='Munari+13 sub')
pylab.plot(scalings.sigma(s, 0, scaling='munari13gal'),
s,
':',
color='orange',
lw=2,
label='Munari+13 gal')
if catalog[-2:] == 'SW':
depth = 'shallow'
elif catalog[-2:] == 'DN':
depth = 'deep'
msg = '{0} {1}'.format(catalog[:4], depth)
pylab.annotate(msg,
xy=(0.04, 0.96),
xycoords='axes fraction',
ha='left',
va='top')
l = pylab.legend(loc='lower right')
l.get_frame().set_alpha(0)
pylab.ylim(80, 2000)
pylab.xlim(1e13, 2e15)
return