def getRs(clusterz, mass, errs):
median_rs = nfwutils.RsMassInsideR(mass, 4.0, clusterz, 1.5)
low_rs = nfwutils.RsMassInsideR(mass - errs[0], 4.0, clusterz, 1.5)
high_rs = nfwutils.RsMassInsideR(mass + errs[1], 4.0, clusterz, 1.5)
return median_rs, np.array([median_rs - low_rs, high_rs - median_rs])
def nloglike(Mguess):
#units of 1e14
Mguess = Mguess * 1e14
rs_guess = nfwutils.RsMassInsideR(Mguess, c, zcluster, massrad)
return -pdzperturbtools.nloglike_loop(r_points, z_points, ghat_points,
betas, pdz, gsig, rs_guess, c,
zcluster)
def r_scale(mass=parts.mass_15mpc,
concentration=parts.concentration,
zcluster=parts.zcluster):
try:
rs = nfwutils.RsMassInsideR(mass, concentration, zcluster, 1.5)
except ValueError:
raise pymc.ZeroProbability
return rs
def createCutoutSuite(zs,
massrange,
goodbpz,
sizes,
snratios,
outputdir,
simcats=None,
sourcecat=None,
shape_distro=__DEFAULT_SHAPE_DISTRO__,
shape_distro_kw_sets=100 * [{
'sigma': 0.25
}],
idcol='ID'):
if simcats is None:
simcats = []
for i in range(len(shape_distro_kw_sets)):
simsource = extractField(sourcecat, sizes, snratios)
simcats.append(simsource)
for curz in zs:
print 'z = %2.2f' % curz
for cur_mass in massrange:
print '\tmass = %2.2f' % (cur_mass / 1e14)
for i, simsource, kw_set in zip(range(len(simcats)), simcats,
shape_distro_kw_sets):
print '\t\t%d' % i
#adam-old# base = '%s/cutout_z=%1.2f_mass=%2.2f_%d' % (outputdir, curz, cur_mass / 1e14, i)
base = '%s/cutout_z_drawn_z=%1.2f_mass=%2.2f_%d' % (
outputdir, curz, cur_mass / 1e14, i)
#cur_rs = nfwutils.rscaleConstM(cur_mass, 4.0, curz, 500)
cur_rs = nfwutils.RsMassInsideR(cur_mass, 4.0, curz, 1.5)
simsource, simbpz = commonSubset(simsource, goodbpz, id2=idcol)
simcat, momento = createCatalog(simbpz,
simsource['size'],
simsource['snratio'],
4.0,
cur_rs,
curz,
ngals=None,
shape_distro=shape_distro,
shape_distro_kw=kw_set,
radii_pix=simsource['r_pix'],
idcol=idcol)
simcat.saveas('%s.cat' % base, clobber=True)
output = open('%s.momento' % base, 'wb')
cPickle.dump(momento, output, -1)
output.close()
def readCCSummary_diffR(dir,
clusters,
bootrange,
diffR,
cluster_zs,
concentration=4.):
masses = {}
mask = {}
for cluster in clusters:
workdir = '%s/%s' % (dir, cluster)
zcluster = cluster_zs[cluster]
masses[cluster] = np.zeros(len(bootrange))
mask[cluster] = np.ones(len(bootrange))
for i, bootnum in enumerate(bootrange):
inputfile = '%s/bootstrap_%d.cc.out' % (workdir, bootnum)
if not os.path.exists(inputfile):
print inputfile
mask[cluster][i] = 0
masses[cluster][i] = -1
continue
input = open(inputfile)
for line in input.readlines():
if cc_regex.match(line):
tokens = line.split()
mass = float(tokens[-1])
if mass == 0.:
mass = 1e13
rscale = nfwutils.RsMassInsideR(mass, concentration,
zcluster, 1.5)
masses[cluster][i] = nfwutils.massInsideR(
rscale, concentration, zcluster, diffR)
break
input.close()
print cluster, len(masses[cluster])
return masses, mask
def createNLikelihood(mcluster,
zcluster,
z0,
sigz0,
rmin=0.75,
rmax=3.0,
c=4.0,
massrad=1.5,
gsig=0.25,
npoints=200000):
rs = nfwutils.RsMassInsideR(mcluster, c, zcluster, massrad)
linmin = rmin / np.sqrt(2)
linmax = rmax / np.sqrt(2)
r_points = np.sqrt(
np.random.uniform(linmin, linmax, size=npoints)**2 +
np.random.uniform(linmin, linmax, size=npoints)**2)
z_points = z0 + sigz0 * np.random.standard_normal(npoints)
gamma_inf = nfwmodeltools.NFWShear(r_points, c, rs, zcluster)
kappa_inf = nfwmodeltools.NFWKappa(r_points, c, rs, zcluster)
beta_s = nfwutils.beta_s(z_points, zcluster)
g0 = beta_s * gamma_inf / (1 - beta_s * kappa_inf)
ghat_points = g0 + gsig * np.random.standard_normal(npoints)
dzt = 0.01
pdzrange = np.arange(z0 - 5 * sigz0, z0 + 5 * sigz0, dzt)
pdz = stats.Gaussian(pdzrange, z0, sigz0) * dzt
betas = np.array(nfwutils.beta_s(pdzrange, zcluster))
def nloglike(Mguess):
#units of 1e14
Mguess = Mguess * 1e14
rs_guess = nfwutils.RsMassInsideR(Mguess, c, zcluster, massrad)
return -pdzperturbtools.nloglike_loop(r_points, z_points, ghat_points,
betas, pdz, gsig, rs_guess, c,
zcluster)
return nloglike
def stackClusters(data = None, doPlot = True, cosmology = nfwutils.std_cosmology):
workdir = '/u/ki/dapple/ki06/catalog_backup_2012-02-08'
outdir = '/u/ki/dapple/subaru/doug/publication/baseline_2012-02-08'
if data is None:
data = {}
if 'items' not in data:
data['items'] = readtxtfile('worklist')
items = data['items']
if 'zbins' not in data:
data['zbins'] = np.unique(np.hstack([np.linspace(0., 1., 3.), np.linspace(1., 5., 10.), np.linspace(5., 10., 5.)]))
zbins = data['zbins']
bincenters = (zbins[1:] + zbins[:-1])/2.
if 'clusters' not in data:
clusters = {}
for cluster, filter, image in items:
key = (cluster, filter, image)
clusters[key] = {}
controller = driver.makeController()
options, args = controller.modelbuilder.createOptions()
options, args = controller.filehandler.createOptions(options = options, args = args,
workdir = workdir,
incatalog = '%s/%s.%s.%s.lensingbase.cat' % (workdir, cluster, filter, image),
cluster = cluster, filter = filter, image = image,
shapecut = True,
redseqcat = '%s/%s.%s.%s.redsequence.cat' % (workdir, cluster, filter, image))
controller.load(options, args)
stats = cPickle.load(open('%s/%s.%s.%s.out.mass15mpc.mass.summary.pkl' % (outdir, cluster, filter, image)))
mass = stats['quantiles'][50]
rs = nfwutils.RsMassInsideR(mass, 4.0, controller.zcluster, 1.5)
scaledZ, estimators = sr.scaleShear(controller, rs, 4.0, cosmology = cosmology)
bins, weights, aveshear = sr.calcZBinWeights(scaledZ, controller.pdz, estimators, zbins)
clusters[key]['weights'] = weights
clusters[key]['shears'] = aveshear
data['clusters'] = clusters
else:
clusters = data['clusters']
if 'median' not in data:
allweights = np.vstack([clusters[tuple(item)]['weights'] for item in items])
allshears = np.vstack([clusters[tuple(item)]['shears'] for item in items])
median, sig1, sig2 = sr.calcBinDistro(zbins, allweights, allshears)
data['median'] = median
data['sig1'] = sig1
data['sig2'] = sig2
else:
median = data['median']
sig1 = data['sig1']
sig2 = data['sig2']
if doPlot:
fig = pylab.figure()
ax = fig.add_axes([0.12, 0.12, 0.95 - 0.12, 0.95 - 0.12])
ax.errorbar(bincenters, median, sig2, fmt='bo')
ax.errorbar(bincenters, median, sig1, fmt='ro')
xplot = np.arange(0., np.max(zbins), 0.01)
ax.plot(xplot, sr.shearScaling(xplot), 'k-', linewidth=1.5)
ax.set_xlabel('Scaled Redshift')
ax.set_ylabel('Lensing Power')
ax.set_title('All Cluster Stack -- Restricted to Fit Data')
fig.savefig('notes/shearratio/stack_restricted_manypoints.pdf')
return fig, data
return data
def calcBootstrapCovar(bootstrap):
sourcedir = '/u/ki/dapple/ki06/catalog_backup_2012-02-08'
bootdir = '/u/ki/dapple/ki06/bootstrap_2012-02-08'
data = {}
if 'items' not in data:
data['items'] = readtxtfile('worklist')
items = data['items']
if 'zbins' not in data:
data['zbins'] = np.unique(np.hstack([np.linspace(0., 1., 3.), np.linspace(1., 5., 10.), np.linspace(5., 10., 5.)]))
zbins = data['zbins']
bincenters = (zbins[1:] + zbins[:-1])/2.
if 'clusters' not in data:
clusters = {}
for cluster, filter, image in items:
key = (cluster, filter, image)
clusters[key] = {}
clusterdir='%s/%s' % (bootdir, cluster)
i = bootstrap
controller = driver.makeController()
options, args = controller.modelbuilder.createOptions()
options, args = controller.filehandler.createOptions(options = options, args = args,
workdir = sourcedir,
incatalog = '%s/bootstrap_%d.ml.cat' % (clusterdir, i),
cluster = cluster, filter = filter, image = image)
controller.load(options, args)
stats = cPickle.load(open('%s/bootstrap_%d.ml.out.mass15mpc.mass.summary.pkl' % (clusterdir, i)))
mass = stats['quantiles'][50]
rs = nfwutils.RsMassInsideR(mass, 4.0, controller.zcluster, 1.5)
scaledZ, estimators = sr.scaleShear(controller, rs, 4.0)
bins, weights, aveshear = sr.calcZBinWeights(scaledZ, controller.pdz, estimators, zbins)
clusters[key]['weights'] = weights
clusters[key]['shears'] = aveshear
data['clusters'] = clusters
else:
clusters = data['clusters']
if 'medians' not in data:
clusterboot = np.random.randint(0, len(items), len(items))
allweights = np.vstack([clusters[tuple(items[j])]['weights'] for j in clusterboot])
allshears = np.vstack([clusters[tuple(items[j])]['shears'] for j in clusterboot])
median, sig1, sig2 = sr.calcBinDistro(zbins, allweights, allshears)
data['median'] = median
data['sig1'] = sig1
data['sig2'] = sig2
return data
def plotOneCluster(cluster, filter, image, workdir = '/u/ki/dapple/ki06/catalog_backup_2012-02-08',
outdir = '/u/ki/dapple/subaru/doug/publication/baseline_2012-02-08', data = None):
if data is None:
data = {}
if 'controller' not in data:
controller = driver.makeController()
options, args = controller.modelbuilder.createOptions(zcut = None)
options, args = controller.filehandler.createOptions(options = options, args = args,
workdir = workdir,
incatalog = '%s/%s.%s.%s.lensingbase.cat' % (workdir, cluster, filter, image),
cluster = cluster, filter = filter, image = image,
shapecut = True,
redseqcat = '%s/%s.%s.%s.redsequence.cat' % (workdir, cluster, filter, image))
controller.load(options, args)
data['controller'] = controller
else:
controller = data['controller']
if 'rs' not in data:
stats = cPickle.load(open('%s/%s.%s.%s.out.mass15mpc.mass.summary.pkl' % (outdir, cluster, filter, image)))
mass = stats['quantiles'][50]
rs = nfwutils.RsMassInsideR(mass, 4.0, controller.zcluster, 1.5)
data['rs'] = rs
else:
rs = data['rs']
if 'median' not in data:
scaledZ, estimators = sr.scaleShear(controller, rs, 4.0)
bins = np.unique(np.hstack([np.linspace(0., 1., 3.), np.linspace(1., np.max(scaledZ), 5.)]))
scaledZbins, weights, aveEst = sr.calcZBinWeights(scaledZ, controller.pdz, estimators, bins)
median, sig1, sig2 = sr.calcBinDistro(scaledZbins, weights, aveEst)
data['bins'] = bins
data['median'] = median
data['sig1'] = sig1
data['sig2'] = sig2
else:
bins = data['bins']
median = data['median']
sig1 = data['sig1']
sig2 = data['sig2']
fig = pylab.figure()
ax = fig.add_axes([0.12, 0.12, 0.95 - 0.12, 0.95 - 0.12])
bincenters = (bins[1:] + bins[:-1])/2.
ax.errorbar(bincenters, median, sig2, fmt='bo')
ax.errorbar(bincenters, median, sig1, fmt='ro')
xplot = np.arange(0., np.max(bins), 0.01)
ax.plot(xplot, sr.shearScaling(xplot), 'k-', linewidth=1.5)
ax.set_xlabel('Scaled Redshift')
ax.set_ylabel('Lensing Power')
ax.set_title('%s %s %s' % (cluster, filter, image))
fig.savefig('notes/shearratio/%s.%s.%s.pdf' % (cluster, filter, image))
return fig, data
def stackClusters(data = None, cosmology = nfwutils.std_cosmology, outdir = '/u/ki/dapple/subaru/doug/publication/baseline_2012-05-17'):
workdir = '/u/ki/dapple/ki06/catalog_backup_2012-05-17'
if data is None:
data = {}
if 'items' not in data:
data['items'] = readtxtfile('worklist')
items = data['items']
if 'clusters' not in data:
clusters = {}
for cluster, filter, image in items:
key = (cluster, filter, image)
clusters[key] = {}
controller = driver.makeController()
options, args = controller.modelbuilder.createOptions(zcut= None)
options, args = controller.filehandler.createOptions(options = options, args = args,
workdir = workdir,
incatalog = '%s/%s.%s.%s.lensingbase.cat' % (workdir, cluster, filter, image),
cluster = cluster, filter = filter, image = image,
shapecut = True,
redseqcat = '%s/%s.%s.%s.redsequence.cat' % (workdir, cluster, filter, image))
controller.load(options, args)
stats = cPickle.load(open('%s/%s.%s.%s.out.mass15mpc.mass.summary.pkl' % (outdir, cluster, filter, image)))
mass = stats['quantiles'][50]
rs = nfwutils.RsMassInsideR(mass, 4.0, controller.zcluster, 1.5)
scaledZ, estimators = sr.scaleShear(controller, rs, 4.0, cosmology = cosmology)
clusters[key]['scaledZ'] = scaledZ
clusters[key]['estimators'] = estimators
clusters[key]['pdz'] = controller.pdz
data['clusters'] = clusters
else:
clusters = data['clusters']
maxScaledZ = -1
for key in clusters.keys():
localMax = np.max(clusters[key]['scaledZ'])
maxScaledZ = max(localMax, maxScaledZ)
if 'zbins' not in data:
zbins = np.unique(np.hstack([np.linspace(0., 1., 3.), np.logspace(0.1, np.log10(maxScaledZ), 12.)]))
data['zbins'] = np.hstack([zbins[:-3], zbins[-1]])
zbins = data['zbins']
bincenters = (zbins[1:] + zbins[:-1])/2.
for key in clusters.keys():
bins, weights, aveshear = sr.calcZBinWeights(clusters[key]['scaledZ'], clusters[key]['estimators'], zbins)
clusters[key]['weights'] = weights
clusters[key]['shears'] = aveshear
if 'maxlike' not in data:
allweights = np.vstack([clusters[tuple(item)]['weights'] for item in items])
allshears = np.vstack([clusters[tuple(item)]['shears'] for item in items])
pointest = sr.calcBinDistro(zbins, allweights, allshears)
data['pointest'] = pointest
maxlike, sig1, sig2, maxlike_ests = sr.bootstrapBinDistro(zbins, allweights, allshears)
data['maxlike'] = maxlike
data['sig1'] = sig1
data['sig2'] = sig2
data['maxlike_ests'] = maxlike_ests
else:
maxlike = data['maxlike']
sig1 = data['sig1']
sig2 = data['sig2']
fig = pylab.figure()
ax = fig.add_axes([0.12, 0.12, 0.95 - 0.12, 0.95 - 0.12])
ax.errorbar(bincenters, maxlike, sig2, fmt='bo')
ax.errorbar(bincenters, maxlike, sig1, fmt='ro')
xplot = np.arange(0.01, np.max(zbins), 0.01)
ax.plot(xplot, sr.shearScaling(xplot), 'k-', linewidth=1.5)
ax.set_xlabel('$x = \omega_s/\omega_l$')
ax.set_ylabel('$\gamma(z)/\gamma(\infty)$')
ax.set_title('All Cluster Stack -- Maxlike Point Est')
fig.savefig('notes/shearratio/stack_maxlike.pdf')
return fig, data
