def stackCats(stackfile, configname, answerfile, outfile):
filebase = os.path.basename(answerfile)
match = re.match('(.+)_answers.pkl', filebase)
simtype = match.group(1)
with open(answerfile, 'rb') as input:
answers = cPickle.load(input)
tostack = [x[0] for x in readtxtfile.readtxtfile(stackfile)]
config = nfwfit.readConfiguration(configname)
simreader = nfwfit.buildSimReader(config)
nfwutils.global_cosmology.set_cosmology(simreader.getCosmology())
fitter = nfwfit.buildFitter(config)
profile = fitter.profileBuilder
if profile.binspacing == 'linear':
binedges = np.linspace(profile.minradii, profile.maxradii,
profile.nbins + 1)
else:
binedges = np.logspace(np.log10(profile.minradii),
np.log10(profile.maxradii), profile.nbins + 1)
stackedprofile = OnlineStatistics(binedges)
for catalogname in tostack:
filebase = os.path.basename(catalogname)
match = idpatterns[simtype].match(filebase)
haloid = int(match.group(1))
try:
truth = answers[haloid]
except KeyError:
print 'Failure at {0}'.format(output)
raise
catalog = nfwfit.readSimCatalog(catalogname, simreader, config)
stackedprofile.accumulate(catalog, truth)
stackedprofile.writeSimCat(outfile)
return stackedprofile
def createFakeChains(config,
nclusters,
zcluster,
r_mpc_edges,
beta_s,
galdensity,
shapenoise,
nsamples=1000,
mass=10**15.2):
mtrues = mass * np.ones(nclusters)
r_mpcs, shearprofiles, shearerrs = createClusterSet(
config, mtrues, zcluster, r_mpc_edges, beta_s, galdensity, shapenoise)
fitter = nfwfit.buildFitter(config)
chains = []
for i in range(nclusters):
mcmc_model = None
for j in range(10):
try:
mcmc_model = fitter.model.makeMCMCModel(
r_mpcs[i], shearprofiles[i], shearerrs[i], beta_s,
beta_s**2, zcluster)
break
except pymc.ZeroProbability:
pass
if mcmc_model is None:
raise pymc.ZeroProbability
manager = varcontainer.VarContainer()
options = varcontainer.VarContainer()
manager.options = options
options.singlecore = True
options.adapt_every = 100
options.adapt_after = 100
options.nsamples = nsamples
manager.model = mcmc_model
runner = pma.MyMCMemRunner()
runner.run(manager)
runner.finalize(manager)
chains.append(manager.chain['m200'][200:])
return mtrues, chains
def fitClusterSet(config, zcluster, r_mpcs, beta_s, shearprofiles, shearerrs):
fitter = nfwfit.buildFitter(config)
fitMasses = np.zeros(len(shearprofiles))
fitErrs = np.zeros(len(shearprofiles))
mask = np.ones(len(shearprofiles))
for i in range(len(shearprofiles)):
fitres = fitter.minChisqMethod(r_mpcs[i], shearprofiles[i],
shearerrs[i], beta_s, beta_s**2,
zcluster)
if fitres is None:
fitres = fitter.minChisqMethod(r_mpcs[i],
shearprofiles[i],
shearerrs[i],
beta_s,
beta_s**2,
zcluster,
useSimplex=True)
if fitres is None:
mask[i] = 0
else:
fitMasses[i] = fitres[0]['m200'] * fitter.model.massScale
# fitMasses[i] = nfwutils.Mdelta(fitres[0]['rscale'], 4.0, zcluster, 200.)
asymerrs = fitres[1]['m200']
try:
fitErrs[i] = fitter.model.massScale * (asymerrs[1] -
asymerrs[0]) / 2.
except TypeError:
fitErrs[i] = asymerrs
#
# mask = (mask == 1)
return fitMasses, fitErrs, mask
def plotLogProbSurface(catalogname,
configfile,
fig=None,
noiselevels=[0., 0.03, 0.07, 0.15]):
if fig is None:
fig = pylab.figure()
config = nfwfit.readConfiguration(configfile)
simreader = nfwfit.buildSimReader(config)
nfwutils.global_cosmology.set_cosmology(simreader.getCosmology())
catalog = nfwfit.readSimCatalog(catalogname, simreader, config)
fitter = nfwfit.buildFitter(config)
r_mpc, ghat, sigma_ghat, beta_s, beta_s2, zlens = fitter.prepData(
catalog, config)
print sigma_ghat
fitter.model.setData(beta_s, beta_s2, zlens)
guess = fitter.model.guess()
massgrid = np.arange(1e14, 1e15, 2.5e13) / fitter.model.massScale
masscenters = (massgrid[1:] + massgrid[:-1]) / 2.
for noise in noiselevels:
noisy_g = ghat + noise * np.random.standard_normal(len(ghat))
noisy_sigma = np.sqrt(sigma_ghat**2 + noise**2)
if len(guess) == 2:
concengrid = np.arange(1., 15., 0.25)
concencenters = (concengrid[1:] + concengrid[:-1]) / 2.
chisqgrid = np.zeros((len(masscenters), len(concencenters)))
for i in range(len(masscenters)):
for j in range(len(concencenters)):
chisqgrid[i, j] = fitmodel.ChiSqStat(
noisy_g, noisy_sigma,
fitter.model(r_mpc, masscenters[i], concencenters[j]))
probgrid = np.exp(-0.5 * (chisqgrid - np.max(chisqgrid)))
massprobgrid = np.sum(probgrid, axis=1) / np.sum(probgrid)
logprob = np.log(massprobgrid)
else:
chisqgrid = np.zeros(len(masscenters))
for i in range(len(massgrid)):
chisqgrid[i] = fitmodel.ChiSqStat(
noisy_g, noisy_sigma, fitter.model(r_mpc, masscenters[i]))
logprob = -0.5 * chisqgrid
ax = pylab.gca()
ax.plot(masscenters, np.exp(logprob - np.max(logprob)), label=noise)
print 'Max: {0}'.format(masscenters[np.argmax(logprob)])
pylab.legend()
return fig, massgrid, concengrid, chisqgrid, logprob
def consolidateFits(workdir, simtype, outdir):
failfile = open('{0}/fails'.format(outdir), 'w')
idpattern = idpatterns[simtype]
answers = cPickle.load(
open('{0}/{1}_answers.pkl'.format(workdir, simtype), 'rb'))
outputfiles = glob.glob('%s/*.out' % outdir)
nhalos = len(outputfiles)
ids = []
measured_m200s = np.zeros(nhalos)
measured_m200errs = np.zeros(nhalos)
measured_m500s = np.zeros(nhalos)
measured_m500errs = np.zeros(nhalos)
measured_cs = np.zeros(nhalos)
measured_rs = np.zeros(nhalos)
true_m200s = np.zeros(nhalos)
true_m500s = np.zeros(nhalos)
true_cs = np.zeros(nhalos)
redshifts = np.zeros(nhalos)
results = dict(ids=ids,
measured_m200s=measured_m200s,
measured_m200errs=measured_m200errs,
measured_m500s=measured_m500s,
measured_m500errss=measured_m500errs,
measured_cs=measured_cs,
measured_rs=measured_rs,
true_m200s=true_m200s,
true_m500s=true_m500s,
true_cs=true_cs,
redshifts=redshifts)
class WeirdException(Exception):
pass
#load up the environment for cosmology, and mc relation if used
config = nfwfit.readConfiguration('{0}/config.sh'.format(outdir))
simreader = nfwfit.buildSimReader(config)
nfwutils.global_cosmology.set_cosmology(simreader.getCosmology())
fitter = nfwfit.buildFitter(config)
configname = os.path.basename(outdir)
for i, output in enumerate(outputfiles):
filebase = os.path.basename(output)
match = idpattern.match(filebase)
try:
haloid = int(match.group(1))
except AttributeError as e:
print filebase
raise e
except ValueError:
haloid = match.group(1)
try:
truth = answers[haloid]
except KeyError:
print 'Failure at {0}'.format(output)
raise
ids.append(haloid)
true_m200s[i] = truth['m200']
true_m500s[i] = truth['m500']
true_cs[i] = truth['concen']
redshifts[i] = truth['redshift']
input = open(output)
measured = cPickle.load(input)
input.close()
if measured is None:
print 'Fail {0} {1}'.format(configname, haloid)
failfile.write('Fail {0} {1}\n'.format(configname, haloid))
continue
measured_m200s[i] = measured[0][
'm200'] * fitter.model.massScale * nfwutils.global_cosmology.h
measured_m200errs[i] = np.mean(np.abs(
measured[1]
['m200'])) * fitter.model.massScale * nfwutils.global_cosmology.h
if 'c200' in measured:
measured_cs[i] = measured['c200']
else:
## need to dig up the mc relation
measured_cs[i] = fitter.model.massconRelation(
np.abs(measured_m200s[i]), redshifts[i],
fitter.model.overdensity)
#####
#calculate m500
measured_rs[i] = nfwutils.rscaleConstM(np.abs(measured_m200s[i]),
measured_cs[i], redshifts[i],
fitter.model.overdensity)
measured_m500s[i] = nfwutils.Mdelta(measured_rs[i], measured_cs[i],
redshifts[i], 500)
if measured_m200s[i] < 0:
measured_m500s[i] = -measured_m500s[i]
if not np.isfinite(measured_m500s[i]):
print 'NOT FINITE'
print haloid
print measured
cPickle.dump(results, open('%s/consolidated.pkl' % outdir, 'w'))
failfile.close()
def compareNoiseProfiles(data=None):
''' Compare intrinsic noise levels to assumed shape noise'''
if data is None:
data = {}
if 'centers_mpc' not in data:
config = nfwfit.readConfiguration(
'/vol/euclid1/euclid1_1/dapple/mxxl_lensing/mxxlsnap54/general-c4-r10-n0_0-xrayNONE/config.sh'
)
simreader = nfwfit.buildSimReader(config)
nfwutils.global_cosmology.set_cosmology(simreader.getCosmology())
fitter = nfwfit.buildFitter(config)
intrnoise_profiles = []
for haloid in range(800, 880):
catbase = '/vol/euclid1/euclid1_raid1/dapple/mxxl_lensing/mxxlsnap54/halo_54_{}_0'.format(
haloid)
catalog = nfwfit.readSimCatalog(catbase, simreader, config)
r_mpc, ghat, sigma_ghat, beta_s, beta_s2, zlens = fitter.prepData(
catalog)
intrnoise_profiles.append(sigma_ghat)
intrnoise_profiles = np.row_stack(intrnoise_profiles)
intrnoise = np.mean(intrnoise_profiles, axis=0)
intrnoise_err = np.std(intrnoise_profiles, axis=0)
edges_mpc = np.linspace(config.profilemin, config.profilemax,
config.nbins + 1)
centers_mpc = (edges_mpc[1:] + edges_mpc[:-1]) / 2.
dL = nfwutils.global_cosmology.angulardist(zlens)
edges_arcmin = (edges_mpc / dL) * (180 / np.pi) * 60
bin_areas = np.pi * (edges_arcmin[1:]**2 - edges_arcmin[:-1]**2)
shapesigma = 0.25
galdensity = 20. # per sq arc min
shape_noise = shapesigma / np.sqrt(galdensity * bin_areas)
data['centers_mpc'] = centers_mpc
data['intrnoise'] = intrnoise
data['intrnoise_err'] = intrnoise_err
data['shape_noise'] = shape_noise
else:
centers_mpc = data['centers_mpc']
intrnoise = data['intrnoise']
intrnoise_err = data['intrnoise_err']
shape_noise = data['shape_noise']
fig = pylab.figure()
ax = pylab.gca()
ax.plot(centers_mpc,
intrnoise / intrnoise[2],
label='Intrinsic Noise [80 cluster avg]',
marker='None',
linestyle='-',
linewidth=2,
color=pp.colors[0])
ax.plot(centers_mpc,
shape_noise / shape_noise[2],
marker='None',
linestyle='-',
linewidth=3,
color=pp.colors[1],
label='Shape Noise')
ax.legend(loc='upper right')
ax.set_xlabel('Radius [Mpc]', fontsize=16)
ax.set_ylabel('Relative Shear Error [Arbit Norm]', fontsize=16)
fig.tight_layout()
fig.savefig('figures/relative_shear_noise.png')
fig.savefig('figures/relative_shear_noise.pdf')
fig.savefig('figures/relative_shear_noise.eps')
return fig, data
def massapp(catalog, config, nfwconfig):
zcluster = catalog.hdu.header['ZLENS']
dL = nfwutils.global_cosmology.angulardist(zcluster)
r2 = config.massappr2
controlbins = config.controlbins
minradii = config.profilemin
rmax = config.profilemax
nbins = config.nbins
# fit NFW profile
nfwfitter = nfwfit.buildFitter(nfwconfig)
nfwm200, nfwm200err = nfwfitter.runUntilNotFail(catalog, config)
nfwm200 = nfwm200['m200']
c200 = nfwfitter.model.massconRelation(np.abs(nfwm200)*nfwfitter.model.massScale*nfwutils.global_cosmology.h, nfwfitter.model.zcluster, nfwfitter.model.overdensity)
rho_c = nfwutils.global_cosmology.rho_crit(zcluster)
rho_c_over_sigma_c = 1.5 * dL * nfwutils.global_cosmology.beta([1e6], zcluster)[0] * nfwutils.global_cosmology.hubble2(zcluster) / nfwutils.global_cosmology.v_c**2
nfwrscale = tools.rscaleConstM(nfwm200,
c200,
rho_c,
200)
# calculate gamma for catalog
#use kappa from best fit nfw profile
nfwkappa = tools.NFWKappa(np.ascontiguousarray(catalog['r_mpc'], dtype='<d'), c200, nfwrscale, rho_c_over_sigma_c)
gamma = catalog['ghat']*(1-catalog['beta_s']*nfwkappa)/catalog['beta_s']
radii, shear, shearerr, avebeta, avebeta2, ngals = logbinning(catalog, gamma, minradii, r2, nbins)
cradii, cshear, cshearerr, cavebeta, cavebeat2, cngals = logbinning(catalog, gamma, r2, rmax, controlbins)
integrand2 = cshear/cradii
int2 = 2*rmax**2*scipy.integrate.simps(integrand2, cradii)/(rmax**2 - r2**2)
#kappa aperture
kappa_ap = avekappa(r2, rmax, nfwrscale, c200, rho_c_over_sigma_c)
r1s = radii
kappa_proj = np.zeros_like(r1s)
matching_m200s = np.zeros_like(r1s)
mass_enclosed = np.zeros_like(r1s)
density_enclosed = np.zeros_like(r1s)
for cur_ap_index, r1 in enumerate(r1s):
#gamma integrals
integrand1 = (shear/radii)[cur_ap_index:]
res = scipy.integrate.simps(integrand1, radii[cur_ap_index:])
int1 = 2*res
zeta_c = int1 + int2
#find best matched nfw that reproduces kappa core
kappa_r1 = zeta_c + kappa_ap
kappa_proj[cur_ap_index] = kappa_r1
##
def findNFW(m200):
c200 = nfwfitter.model.massconRelation(np.abs(m200)*nfwfitter.model.massScale*nfwutils.global_cosmology.h, nfwfitter.model.zcluster, nfwfitter.model.overdensity)
nfwrscale = tools.rscaleConstM(m200,
c200,
rho_c,
200)
avekappa = tools.aveEnclosedKappa(np.array([r1], dtype=np.double),
c200,
nfwrscale,
rho_c_over_sigma_c)
return avekappa - kappa_r1
##
best_m200 = scipy.optimize.brentq(findNFW, 5e13, 1e16)
matching_m200s[cur_ap_index] = best_m200
best_c200 = nfwfitter.model.massconRelation(np.abs(best_m200)*nfwfitter.model.massScale*nfwutils.global_cosmology.h, nfwfitter.model.zcluster, nfwfitter.model.overdensity)
best_nfwrscale = tools.rscaleConstM(best_m200,
best_c200,
rho_c,
200)
mass_enclosed[cur_ap_index] = nfwutils.massInsideR(best_nfwrscale, best_c200,
zcluster, r1)
vol = (4./3)*np.pi*r1**3
density_enclosed[cur_ap_index] = mass_enclosed[cur_ap_index] / vol
return r1s, kappa_proj, matching_m200s, mass_enclosed, density_enclosed
