def load5v6simdata(worklist):
clusters = [x[0] for x in worklist]
redshifts = cm.readClusterRedshifts()
properredshifts = np.array([redshifts[x] for x in clusters])
subdirs = ['contam0p10/%s' % x for x in ['BVRIZ', 'APER']]
fracbiases = ss.processFracBiasData(
'/u/ki/dapple/nfs12/cosmos/simulations/publication/highsn/cluster3',
subdirs, clusters, redshifts)
data = [fracbiases[0], fracbiases[1], properredshifts]
return data
def galdensity(data = None):
if data is None:
data = {}
if 'ngals' not in data:
ngals = cPickle.load(open('galaxy_counts_pzmethod.pkl', 'rb'))
data['ngals'] = ngals
items = [tuple(x) for x in readtxtfile('worklist')]
clusters = [x[0] for x in items]
redshifts = cm.readClusterRedshifts()
properz = np.array([redshifts[x] for x in clusters])
data['properz'] = properz
Dl = np.array([nfwutils.angulardist(z) for z in properz])
data['Dl'] = Dl
inner_rad = np.arctan2(0.75, Dl) * (180./np.pi) * 60
outer_rad = np.arctan2(3., Dl) * (180 / np.pi) * 60
area = np.pi*(outer_rad**2 - inner_rad**2)
data['area'] = area
propercounts = np.array([ngals[x] for x in items])
density = propercounts / area
data['density'] = density
else:
properz = data['properz']
density = data['density']
fig = pylab.figure()
ax = fig.add_axes([0.12, 0.12, 0.95 - 0.12, 0.95 - 0.12])
ax.plot(properz, density, 'bo')
ax.set_xlabel('Cluster Redshift')
ax.set_ylabel('Input Galaxy Density')
return fig, data
def ML5filterContamCompareScript(data=None):
if data is None:
data = {}
if 'worklist' not in data:
data['worklist'] = readtxtfile('simclusterlist')
data['clusters'] = [x[0] for x in data['worklist']]
worklist = data['worklist']
clusters = data['clusters']
if 'redshifts' not in data:
data['redshifts'] = cm.readClusterRedshifts()
redshifts = data['redshifts']
data['properredshifts'] = np.array([redshifts[x] for x in clusters])
redshifts = data['redshifts']
properredshifts = data['properredshifts']
if 'fracbiases' not in data:
subdirs = ['%sBVRIZ' % x for x in ['', 'contam0p10/', 'contam0p20/']]
data['fracbiases'] = ss.processFracBiasData(
'/u/ki/dapple/nfs12/cosmos/simulations/clusters_2012-05-17-highdensity',
subdirs, clusters, redshifts)
fracbiases = data['fracbiases']
fig = publicationContamComparePlot(fracbiases[0], fracbiases[1],
fracbiases[2], properredshifts)
ax = fig.axes[0]
ax.text(0.2, 0.06, r'$B_{\mathrm J}V_{\mathrm J}r^+i^+z^+$', fontsize=16)
fig.savefig('publication/clustersims_bvriz_contam_compare.eps')
return fig, data
def fitAltOffsetScript(data=None):
if data is None:
data = {}
worklist = readtxtfile('worklist')
clusters = [x[0] for x in worklist]
workdir = '/u/ki/dapple/nfs12/cosmos/simulations/clusters_2012-05-17-highdensity/'
subdirs = ['%sBVRIZ' % x for x in ['', 'contam0p10/', 'contam0p20/']]
concentration = 4.
mradius = 1.5
redshifts = cm.readClusterRedshifts()
figs = []
for subdir in subdirs:
if subdir not in data:
data[subdir] = {}
curdata = data[subdir]
if 'masses' not in curdata:
curdata['masses'], errs, massgrid, curdata[
'scale_radii'] = ss.readMLMasses(workdir, subdir, clusters)
masses = curdata['masses']
scale_radii = curdata['scale_radii']
if 'grid' not in curdata:
refmasses = {}
for cluster in clusters:
refmasses[cluster] = nfwutils.massInsideR(
scale_radii[cluster], concentration, redshifts[cluster],
mradius) * np.ones_like(masses[cluster])
curdata['grid'], curdata['means'], curdata[
'scatters'] = isg.intrinsicScatter(
refmasses,
masses,
means=1. + np.arange(-0.08, 0.08, 0.0001),
scatters=np.arange(0.005, 0.05, 0.0025))
means = curdata['means']
scatters = curdata['scatters']
mode, (r68, r95) = isg.getdist_1d_hist(means[0],
means[1],
levels=[0.68, 0.95])
curdata['meandist'] = (mode, r68, r95)
mode, (r68, r95) = isg.getdist_1d_hist(scatters[0],
scatters[1],
levels=[0.68, 0.95])
curdata['scatterdist'] = (mode, r68, r95)
for varname in 'mean scatter'.split():
mode, r68, r95 = curdata['%sdist' % varname]
print mode, r68, r95
print '%s\t%2.4f\t+%2.4f\t-%2.4f\t+%2.4f\t-%2.4f' % (
varname, mode, r68[0][1] - mode, mode - r68[0][0],
r95[0][1] - mode, mode - r95[0][0])
x, prob = curdata['%ss' % varname]
fig = isgp.plotdist_1d_hist(x, prob, mode, [r68[0], r95[0]])
ax = fig.axes[0]
ax.set_title('%s %s' % (subdir, varname))
figs.append(fig)
fig.show()
return figs, data
def PointEstPzScript(data=None):
if data is None:
worklist = readtxtfile('simclusterlist')
clusters = [x[0] for x in worklist]
redshifts = cm.readClusterRedshifts()
properredshifts = np.array([redshifts[x] for x in clusters])
subdirs = ['contam0p10/BVRIZ']
MLfracbias = ss.processFracBiasData(
'/u/ki/dapple/nfs12/cosmos/simulations/clusters_2012-05-17-highdensity',
subdirs, clusters, redshifts)[0]
Bpointmass, Bpointgrid, scale_radii = ss.readPointMasses(
'/u/ki/dapple/nfs12/cosmos/simulations/clusters_2012-05-17',
'contam0p10/newman/BVRIZ', clusters)
truemasses = [
nfwutils.massInsideR(scale_radii[x], 4., redshifts[x], 1.5)
for x in clusters
]
Bpointfracbias = ss.calcFracBias(Bpointgrid, truemasses)
data = [MLfracbias, Bpointfracbias, properredshifts]
else:
MLfracbias = data[0]
Bpointfracbias = data[1]
properredshifts = data[2]
fig = pylab.figure()
try:
ax = fig.add_axes([0.15, 0.12, 0.96 - 0.15, 0.95 - 0.12])
ax.axhline(0.0, c='k', linewidth=1.25)
Apointmean, Apointerr = ss.bootstrapMean(MLfracbias)
Bpointmean, Bpointerr = ss.bootstrapMean(Bpointfracbias)
ax.errorbar(properredshifts - 0.0025,
Bpointmean,
Bpointerr,
fmt='cs',
label=r'Point Estimators',
color='#BFBFD4')
ax.errorbar(properredshifts + 0.0025,
Apointmean,
Apointerr,
fmt='ro',
label=r'P(z) Method')
ax.set_xlim(0.16, 0.72)
ax.set_ylim(-0.08, 0.19)
ax.set_xlabel('Cluster Redshift')
ax.set_ylabel(r'Fractional Mass Bias within 1.5 Mpc')
# ax.text(0.2, 0.12, r'$BVr^{+}i^{+}z^{+}$ Photo-$z$ Point Est', fontsize=16)
ax.legend(loc='upper left', numpoints=1, ncol=1)
fig.savefig('publication/clustersims_pointest_pz_compare.eps')
finally:
return fig, data
def MLPointEstScript(data=None):
if data is None:
worklist = readtxtfile('simclusterlist')
clusters = [x[0] for x in worklist]
redshifts = cm.readClusterRedshifts()
properredshifts = np.array([redshifts[x] for x in clusters])
subdirs = ['contam0p10/BVRIZ']
# MLfracbias = ss.processFracBiasData('/u/ki/dapple/nfs12/cosmos/simulations/publication/highsn/cluster3',
# subdirs, clusters, redshifts)[0]
#
#
# Apointmass, Apointgrid, scale_radii = ss.readPointMasses('/u/ki/dapple/nfs12/cosmos/simulations/publication/highsn/cluster3', 'contam0p10/newman/APER', clusters)
Bpointmass, Bpointgrid, scale_radii = ss.readPointMasses(
'/u/ki/dapple/nfs12/cosmos/simulations/clusters_2012-05-17',
'contam0p10/newman/BVRIZ', clusters)
truemasses = [
nfwutils.massInsideR(scale_radii[x], 4., redshifts[x], 1.5)
for x in clusters
]
# Apointfracbias = ss.calcFracBias(Apointgrid, truemasses)
Bpointfracbias = ss.calcFracBias(Bpointgrid, truemasses)
data = [None, Bpointfracbias, properredshifts]
else:
# Apointfracbias = data[0]
Bpointfracbias = data[1]
properredshifts = data[2]
fig = pylab.figure()
try:
ax = fig.add_axes([0.15, 0.12, 0.95 - 0.15, 0.95 - 0.12])
ax.axhline(0.0, c='k', linewidth=1.25)
# Apointmean, Apointerr = ss.bootstrapMean(Apointfracbias)
Bpointmean, Bpointerr = ss.bootstrapMean(Bpointfracbias)
ax.errorbar(properredshifts,
Bpointmean,
Bpointerr,
fmt='bo',
label=r'$BVr^+i^+z^+$')
# ax.errorbar(properredshifts+0.0025, Apointmean, Apointerr, fmt='rs', label=r'$uBVr^+i^+z^+$')
ax.text(0.166, 0.135, r'$BVr^+i^+z^+$ Photo-Z Point Est', fontsize=16)
ax.set_xlim(0.16, 0.72)
ax.set_ylim(-0.05, 0.15)
ax.set_xlabel('Cluster Redshift', fontsize=16)
ax.set_ylabel(r'Fractional Mass Bias within 1.5 Mpc')
# ax.legend(loc='lower right', numpoints = 1, ncol=2)
fig.savefig('publication/clustersims_pointest_compare.eps')
finally:
return fig, data
def MLUbandRatioScript(data=None):
simlist = readtxtfile('simclusterlist')
noUlist = readtxtfile('noUlist')
worklist = [x for x in simlist if x in noUlist]
del worklist[-1]
clusters = [x[0] for x in worklist]
if data is None:
redshifts = cm.readClusterRedshifts()
properredshifts = np.array([redshifts[x] for x in clusters])
noUsim_masses, noUsim_errs, noUsim_grid, scale_radii = ss.readMLMasses(
'/u/ki/dapple/nfs12/cosmos/simulations/publication/highsn/cluster3',
'contam0p10/BVRIZ', clusters)
Usim_masses, Usim_errs, Usim_grid, scale_radii = ss.readMLMasses(
'/u/ki/dapple/nfs12/cosmos/simulations/publication/highsn/cluster3',
'contam0p10/APER', clusters)
noUmasses, noUmask = cm.readMLBootstraps(
'/u/ki/dapple/ki06/bootstrap_2011-12-14', worklist, np.arange(100))
Umasses, Umask = cm.readMLBootstraps(
'/u/ki/dapple/ki06/bootstrap_U_2012-02-03', worklist,
np.arange(100))
data = [
noUsim_grid, Usim_grid, properredshifts, noUmasses, noUmask,
Umasses, Umask
]
else:
noUsim_grid = data[0]
Usim_grid = data[1]
properredshifts = data[2]
noUmasses = data[3]
noUmask = data[4]
Umasses = data[5]
Umask = data[6]
simRatio = noUsim_grid / Usim_grid
simMean, simErr = ss.bootstrapMean(simRatio.T)
dataRatios = cm.makeRatios(Umasses, Umask, noUmasses, noUmask)
dataMeans, dataErrs = cm.bootstrapMeans(dataRatios, clusters)
fig = pylab.figure()
ax = fig.add_axes([0.14, 0.12, 0.95 - 0.14, 0.95 - 0.12])
ax.errorbar(properredshifts + 0.0025,
dataMeans,
dataErrs,
fmt='rs',
label='Data')
ax.errorbar(properredshifts - 0.0025,
simMean,
simErr,
fmt='bo',
label='Simulations')
ax.axhline(1.0, c='k', linewidth=1.5)
ax.set_xlim(0.16, 0.72)
ax.set_ylim(0.85, 1.35)
ax.set_xlabel('Cluster Redshift')
ax.set_ylabel('Mass without U-band / Mass with U-band')
ax.legend(loc='upper left', numpoints=1)
fig.savefig('publication/dropU_comp.eps')
return fig, data
def calcMasses(clusters, scale_radii, concentration=4.0, mradius=1.5):
redshifts = cm.readClusterRedshifts()
redshifts = [redshifts[x] for x in clusters]
return masses
def precisionZ(data = None):
#mass precision as a function of redshift
if data is None:
data = {}
if 'fracerrs' not in data:
items = [tuple(x) for x in readtxtfile('worklist')]
allmasses = cm.readDougMasses('/u/ki/dapple/subaru/doug/publication/baseline_2012-05-17')
redshifts = cm.readClusterRedshifts()
clusters = [x[0] for x in items]
properz = np.array([redshifts[x] for x in clusters])
masses, errs = cm.constructMassArray(allmasses, items)
fracerrs = errs / masses
aveerrs = np.mean(fracerrs, axis=0)
data['aveerrs'] = aveerrs
data['properz'] = properz
ccitems = [tuple(x) for x in readtxtfile('referenceset')]
ccmasses = cm.readAnjaMasses()
clusters = [x[0] for x in ccitems]
ccproperz = np.array([redshifts[x] for x in clusters])
data['ccproperz'] = ccproperz
masses, errs = cm.constructMassArray(ccmasses, ccitems)
fracerrs = errs/masses
ccaveerrs = np.mean(fracerrs, axis=0)
data['ccaveerrs'] = ccaveerrs
else:
aveerrs = data['aveaerrs']
properz = data['properz']
ccaveerrs = data['ccaveerrs']
ccproperz = data['ccproperz']
fig = pylab.figure()
ax = fig.add_axes([0.12, 0.12, 0.95-0.12, 0.95-0.12])
ax.plot(ccproperz, ccaveerrs, 'bo', label = 'Color-Cut', mfc = 'None', mew = 1.0, mec='b')
ax.plot(properz, aveerrs, 'rD', label = 'P($z$)')
ax.set_xlabel('Cluster Redshift')
ax.set_ylabel('Fractional Statistical Uncertainty M(r$<$1.5Mpc)')
ax.set_xlim([0.14, 0.72])
ax.legend(loc='upper left', numpoints = 1)
fig.savefig('publication/aveerr_redshift.eps')
return fig, data
def lostgals(data = None):
if data is None:
data = {}
items = readtxtfile('worklist')
del items[-1]
clusters = [x[0] for x in items]
if 'properz' not in data:
redshifts = cm.readClusterRedshifts()
properz = np.array([redshifts[x] for x in clusters])
data['properz'] = properz
else:
properz = data['properz']
if 'properbase' not in data:
basecuts = {}
for cluster, filter, image in items:
controller = driver.makeController()
options, args = controller.modelbuilder.createOptions()
options, args = controller.filehandler.createOptions(options = options, args = args,
workdir = '/u/ki/dapple/ki06/catalog_backup_2012-02-08',
incatalog = '/u/ki/dapple/ki06/catalog_backup_2012-02-08/%s.%s.%s.lensingbase.cat' % (cluster, filter, image),
cluster = cluster, filter = filter, image = image,
redseqcat = '/u/ki/dapple/ki06/catalog_backup_2012-02-08/%s.%s.%s.redsequence.cat' % (cluster, filter, image), shapecut = True)
controller.load(options, args)
basecuts[cluster] = controller.ngalaxies
data['basecuts'] = basecuts
properbase = np.array([basecuts[x[0]] for x in items])
data['properbase'] = properbase
else:
properbase = data['properbase']
if 'properloose' not in data:
loosecuts = {}
for cluster, filter, image in items:
controller = driver.makeController()
options, args = controller.modelbuilder.createOptions(deltaz95high = 9999, zbhigh = 9999)
options, args = controller.filehandler.createOptions(options = options, args = args,
workdir = '/u/ki/dapple/ki06/catalog_backup_2012-02-08',
incatalog = '/u/ki/dapple/ki06/catalog_backup_2012-02-08/%s.%s.%s.lensingbase.cat' % (cluster, filter, image),
cluster = cluster, filter = filter, image = image,
redseqcat = '/u/ki/dapple/ki06/catalog_backup_2012-02-08/%s.%s.%s.redsequence.cat' % (cluster, filter, image), shapecut = True)
controller.load(options, args)
loosecuts[cluster] = controller.ngalaxies
data['loosecuts'] = loosecuts
properloose = np.array([loosecuts[x[0]] for x in items])
data['properloose'] = properloose
else:
properloose = data['properloose']
if 'ratio' not in data:
ratio = 1 - (properbase.astype('float64') / properloose)
data['ratio'] = ratio
else:
ratio = data['ratio']
fig = pylab.figure()
ax = fig.add_axes([0.12, 0.12, 0.95 - 0.12, 0.95 - 0.12])
ax.plot(properz, ratio, 'bo')
ax.set_xlim([0.16, 0.72])
ax.set_xlabel('Cluster Redshift')
ax.set_ylabel('Fraction of Catalog Discarded')
return fig, data
import sys
import numpy as np
import compare_masses as cm, intrinsicscatter as isc
import scatter_sims as ss, nfwutils
from dappleutils import readtxtfile
workdir = sys.argv[1]
subdir = sys.argv[2]
outfile = sys.argv[3]
nsamples = int(sys.argv[4])
items = readtxtfile('simclusterlist')
clusters = clusters = [ x[0] for x in items]
redshifts = cm.readClusterRedshifts()
properredshifts = np.array([redshifts[x] for x in clusters])
masses, errs, massgrid, scale_radii = ss.readMLMasses(workdir, subdir, clusters)
truemasses = {}
for cluster in clusters:
truemasses[cluster] = nfwutils.massInsideR(scale_radii[cluster], 4., redshifts[cluster], 1.5)
x = np.hstack([len(masses[c])*[truemasses[c]] for c in clusters])
y = np.hstack([masses[c] for c in clusters])
yerr = np.hstack([errs[c] for c in clusters])
calibmodel = isc.IntrinsicScatter(x, y, yerr)
def table(items, mlsrcdir, output, strInterval=normalStrInterval):
MLmasses = cm.readDougMasses(dir=mlsrcdir)
CCmasses = cm.readAnjaMasses()
nametranslation = readNameTranslation()
filtertranslation = readFilterTranslation()
redshifts = cm.readClusterRedshifts()
cordist = np.hstack([pymc.database.pickle.load('ml_cc_rlog.out.%d' % i).trace('m', -1)[25000:] \
for i in range(1, 6)])
sortlist = sorted([(redshifts[x[0]], x) for x in items])
print sortlist
for clusterz, (cluster, filter, image) in sortlist:
if (cluster, filter, image) not in CCmasses:
print 'Skipping %s %s %s' % (cluster, filter, image)
continue
try:
MLcluster = MLCluster.load(cluster, filter, image, clusterz)
values = {
'cluster':
nametranslation[cluster],
'redshift':
clusterz,
'filter':
filtertranslation[filter],
'rs':
strInterval(MLcluster.getRs()),
'con':
strInterval(MLcluster.getConcentration()),
'mlmass':
strInterval(MLmasses[cluster, filter, image],
fmt='%2.1f',
norm=1e14),
'ccrs':
strInterval(
getRs(clusterz, *CCmasses[(cluster, filter, image)])),
'ccmass':
strInterval(CCmasses[(cluster, filter, image)],
fmt='%2.1f',
norm=1e14)
}
# 'cccor' : strInterval(CCcluster.getCorMass(cordist), fmt='%2.1f', norm = 1e14)}
# 'mlmass' : strInterval(MLcluster.getMass(), fmt = '%2.1f', norm = 1e14),
# 'ccmass' : strInterval(CCcluster.getMass(), fmt = '%2.1f', norm = 1e14),
except (KeyError, IOError):
values = {
'cluster':
nametranslation[cluster],
'redshift':
clusterz,
'filter':
filtertranslation[filter],
'rs':
'-',
'con':
'-',
'mlmass':
'-',
'ccrs':
strInterval(
getRs(clusterz, *CCmasses[(cluster, filter, image)])),
'ccmass':
strInterval(CCmasses[(cluster, filter, image)],
fmt='%2.1f',
norm=1e14)
}
# 'cccor' : strInterval(CCcluster.getCorMass(cordist), fmt='%2.1f', norm = 1e14)}
# output.write('%(cluster)s & %(redshift)1.2f & %(rs)s & %(con)s & %(mlmass)s & %(ccrs)s & %(ccmass)s & %(cccor)s \\\\ \n' % values)
output.write(
'%(cluster)s & %(redshift)1.3f & %(rs)s & %(mlmass)s & %(ccrs)s & %(ccmass)s \\\\ \n'
% values)