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