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 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
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