def plotProfile(datfilebase, c, label, labelZ = True):
mass, concen, redshift = readtxtfile.readtxtfile('%s.dat' % datfilebase)[0]
cat = ldac.openObjectFile('%s.cat' % datfilebase)
zlens = cat.hdu.header['ZLENS']
rscale = nfwutils.rscaleConstM(mass/nfwutils.global_cosmology.h, concen, zlens, 200)
r_mpc = np.ascontiguousarray(cat['r_mpc'], dtype='<d')
clean = r_mpc > 0.05
rho_c_over_sigma_c = 1.5 * nfwutils.global_cosmology.angulardist(zlens) * nfwutils.global_cosmology.beta([1e6], zlens)[0] * nfwutils.global_cosmology.hubble2(zlens) / nfwutils.global_cosmology.v_c**2
gamma = nfwmodeltools.NFWShear(r_mpc[clean], concen, rscale, rho_c_over_sigma_c)
kappa = nfwmodeltools.NFWKappa(r_mpc[clean], concen, rscale,rho_c_over_sigma_c)
gpred = cat['beta_s'][clean]*gamma / (1 - (cat['beta_s2'][clean]*kappa/cat['beta_s'][clean]))
signalpred = gpred / (cat['beta_s'][clean]*nfwutils.global_cosmology.beta([1e6], zlens)*nfwutils.global_cosmology.angulardist(zlens))
# print signalpred
if labelZ:
flabel='%s z=%1.2f' % (label, zlens)
else:
flabel =label
pylab.errorbar(cat['r_mpc']*nfwutils.global_cosmology.h, cat['ghat'], cat['ghatdistrosigma']/(np.sqrt(cat['ndat'])),
linestyle='None', marker='o', color=c, label=flabel)
pylab.plot(cat['r_mpc'][clean]*nfwutils.global_cosmology.h, signalpred, marker='None', linestyle=':', color=c, linewidth=2)
def __init__(self):
self.offsets_kpc = [
x[0] for x in readtxtfile.readtxtfile(
pkg_resources.resource_filename('nfwfitter',
'data/cccp_offsets.dat'))
]
def parseFile(self, filename):
basedir = os.path.dirname(filename)
self.redshift = float(readtxtfile.readtxtfile('%s/redshift' % basedir))
with open(filename) as input:
lines = input.readlines()
interior_particles = int(lines[1].split()[1])
for curline, line in enumerate(lines):
if profile_start_template.search(line) is not None:
break
assert (curline < len(lines))
rawprofile = np.array([map(float,x) for x in [x.split() for x in lines[curline+1:]] \
if x != []])
innermass = interior_particles * m_p
diffMass = rawprofile[:, 3] * m_p
inner_radius = rawprofile[:, 0] / h #Mpc
median_radius = rawprofile[:, 1] / h
outer_radius = rawprofile[:, 2] / h
diff_radius = outer_radius - inner_radius
self.inner_mass = innermass
self.inner_radius = inner_radius
self.median_radius = median_radius
self.outer_radius = outer_radius
self.diff_radius = diff_radius
self.diff_mass = diffMass
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 readMCMC(file):
data = readtxtfile(file)
c = []
rs = []
ll = []
m = []
for i in xrange(len(data)):
for j in xrange(int(data[i, 0])):
ll.append(data[i, 1])
c.append(data[i, 2])
rs.append(data[i, 3])
m.append(data[i, 4])
return MCMC(array(c), array(rs), array(ll), array(m))
def residual(binbase):
mass, concen, redshift = readtxtfile.readtxtfile('%s.dat' % binbase)[0]
cat = ldac.openObjectFile('%s.cat' % binbase)
zlens = cat.hdu.header['ZLENS']
rscale = nfwutils.rscaleConstM(mass/nfwutils.global_cosmology.h, concen, zlens, 200)
gamma = nfwmodeltools.NFWShear(cat['r_mpc'], concen, rscale, zlens)
kappa = nfwmodeltools.NFWKappa(cat['r_mpc'], concen, rscale, zlens)
gpred = cat['beta_s']*gamma / (1 - (cat['beta_s2']*kappa/cat['beta_s']))
fig = pylab.figure()
# pylab.errorbar(cat['r_mpc']*nfwutils.global_cosmology.h, cat['ghat']/gpred, cat['ghatdistrosigma']/(np.sqrt(cat['ndat'])*gpred), fmt='bo')
# pylab.errorbar(cat['r_mpc']*nfwutils.global_cosmology.h, cat['ghat']/gpred, cat['ghatdistrosigma']/(gpred), fmt='bo')
pylab.errorbar(cat['r_mpc'], cat['ghat'], cat['ghatdistrosigma']/np.sqrt(cat['ndat']), fmt='bo')
#
ax = pylab.gca()
ax.set_xscale('log')
pylab.axhline(0.0, c='k', linewidth=2)
pylab.xlabel('Radius [Mpc/h]', fontsize=16)
pylab.ylabel('<g_meas/g_pred> - 1)', fontsize=16)
# pylab.ylabel('<g_meas - g_pred>', fontsize=16)
pylab.axis([0.05, 10, -.55, 0.35])
# ax2 = ax.twinx()
# pylab.plot(cat['r_mpc'], gpred, 'k--')
# ax2.errorbar(cat['r_mpc'], cat['ghat'], cat['ghatdistrosigma']/np.sqrt(cat['ndat']), fmt='rs')
# ax2.set_ylabel('<g_meas - g_pred>', color='r', fontsize=16)
# ax2.set_ylim(-0.2, 0.1)
#
# pylab.title('Redshift=%1.2f Mass=%1.2fx10^14 Concen=%1.2f' % (zlens, mass/1e14, concen))
#
pylab.tight_layout()
pylab.savefig('%s.png' % binbase)
return fig
def summarizeChains(chaindirs, binnum, delta):
mus = []
globalmassbinlow = None
globalmassbinhigh = None
chainfiles = [psd.gatherChainFiles(chaindir, delta, binnum = binnum)[0] \
for chaindir in chaindirs]
assert (len(chainfiles) > 0)
for chainfile in chainfiles:
chain = load_chains.loadChains([chainfile], trim=True)
print chainfile, len(chain['logmu'])
if len(chain['logmu'][0, :]) < 5000:
print 'Skipping'
continue
split = int((chain['logmu'].shape[1] + 1000) / 2.)
splitlen = split - 1000
c1mean = np.mean(chain['logmu'][0, 1000:split])
c1err = np.std(chain['logmu'][0, 1000:split]) / np.sqrt(splitlen)
c2mean = np.mean(chain['logmu'][0, split:])
c2err = np.std(chain['logmu'][0, split:]) / np.sqrt(splitlen)
# assert(np.abs(c1mean - c2mean)/np.sqrt(c1err**2 + c2err**2) < 5.)
massbinlow, massbinhigh = [
x[0] for x in readtxtfile.readtxtfile('%s.massrange' % chainfile)
]
if globalmassbinlow is None:
globalmassbinlow = massbinlow
globalmassbinhigh = massbinhigh
assert (massbinlow == globalmassbinlow
and massbinhigh == globalmassbinhigh)
mu, muerr = ci.maxDensityConfidenceRegion(
np.exp(chain['logmu'][0, 1000::3]))
mus.append(mu)
return np.array(mus)
def loadChains(chainfilenames, trim=False):
chainfiles = [readtxtfile.readtxtfile(x) for x in chainfilenames]
takelength = len(chainfiles[0])
if trim is True:
takelength = np.min(np.array([len(chainfile) for chainfile in chainfiles]))
rawdata = [np.row_stack([map(float, y) for y in x[1:takelength]]) for x in chainfiles]
columns = chainfiles[0][0]
chain = {}
for i, col in enumerate(columns):
chain[col] = np.row_stack([x[:,i] for x in rawdata])
return chain
def configure(self, config):
assert(isinstance(config['betacalcer'], betacalcer.FixedBeta))
assert(isinstance(config['shearnoiser'], shearnoiser.NoNoise))
self.profilefile = config['profilefile']
self.maxradii = config['profileMax']
self.minradii = config['profileMin']
self.binwidth = config['binwidth']
self.profileCol = config['profilecol']
profile = readtxtfile.readtxtfile(self.profilefile)
self.bincenters = np.array([x[0] for x in profile])
self.deltag = np.array([x[2] for x in profile])
self.betas = np.array([x[5] for x in profile]) #not scaled by beta_inf
self.magbinids = np.array([x[-1] for x in profile])
mask = np.logical_and(self.bincenters >= self.minradii,
self.bincenters < self.maxradii)
self.bincenters = self.bincenters[mask]
self.deltag = self.deltag[mask]
self.betas = self.betas[mask]
self.magbinids = self.magbinids[mask]
self.nbins = len(self.bincenters)
self.useAveForCenter = False
if 'centerforbin' in config and config['centerforbin'] == 'ave':
self.useAveForCenter = True
if not isGood:
print 'Reprocessing {}'.format(outfile)
configdir, outputbase = os.path.split(outfile)
haloid, outext = os.path.splitext(outputbase)
nfwfit.runNFWFit('{}/../{}'.format(configdir, haloid),
'{}/config.py'.format(configdir), outfile)
def scanConfigs(simdir, configs):
for config in configs:
configdir = '{}/{}'.format(simdir, config)
outfiles = glob.glob('{}/*.out')
for outfile in outfiles:
verifyOutput(outfile)
if __name__ == '__main__':
simdir = sys.argv[1]
configlist = sys.argv[2]
configs = [x[0] for x in readtxtfile.readtxtfile(configlist)]
scanConfigs(simdir, configs)
def BK11multibinresidual(dirbase):
setBK11()
matplotlib.rcParams['figure.figsize'] = [16,16]
fig = pylab.figure()
curplot = 1
for curm in range(4):
for curc in range(4):
pylab.subplot(4,4,curplot)
colori = 1
for snap in [124,141]:
try:
mass, concen, redshift = readtxtfile.readtxtfile('%s_%d/bk11stack_%d_%d.dat' % (dirbase, snap,
curm, curc))[0]
cat = ldac.openObjectFile('%s_%d/bk11stack_%d_%d.cat' % (dirbase, snap,
curm, curc))
zlens = cat.hdu.header['ZLENS']
rscale = nfwutils.rscaleConstM(mass/nfwutils.global_cosmology.h, concen, zlens, 200)
gamma = nfwmodeltools.NFWShear(cat['r_mpc'], concen, rscale, zlens)
kappa = nfwmodeltools.NFWKappa(cat['r_mpc'], concen, rscale, zlens)
gpred = cat['beta_s']*gamma / (1 - (cat['beta_s2']*kappa/cat['beta_s']))
pylab.errorbar(cat['r_mpc']*nfwutils.global_cosmology.h, cat['ghat'], cat['ghatdistrosigma']/(np.sqrt(cat['ndat'])),
linestyle='None', marker='o', color=c[colori], label='M=%1.1fx10^14' % (mass/1e14))
pylab.plot(cat['r_mpc']*nfwutils.global_cosmology.h, gpred, 'k-', linewidth=2)
ax = pylab.gca()
ax.set_xscale('log')
pylab.axhline(0.0, c='k', linewidth=2)
pylab.axis([0.05, 10, -0.03, 0.4])
ax = pylab.gca()
ax.set_xscale('log')
pylab.axhline(0.0, c='k', linewidth=2)
# pylab.legend(loc='lower center', fontsize=10)
# pylab.title('M=%1.1fx10^14 C=%1.1f' % ( mass/1e14, concen))
except:
pass
colori+= 1
curplot += 1
for i in range(4):
pylab.subplot(4,4,13+i)
pylab.xlabel('Radius [Mpc/h]')
pylab.subplot(4,4,4*i+1)
pylab.ylabel('<g_m/g_p - 1>')
pylab.tight_layout()
pylab.savefig('%s_multibin_shearprofile.png' % dirbase)
# pylab.savefig('%s_multibin_resid.png' % binbase)
return fig
def __init__(self):
self.xvp_offsets_mpc = readtxtfile.readtxtfile(
pkg_resources.resource_filename('nfwfitter',
'data/sptxvp_bcgxray'))[:, 0]
def __init__(self):
self.sz_xvp_bcg_offsets_deg = readtxtfile.readtxtfile(
pkg_resources.resource_filename('nfwfitter',
'data/sptxvp_bcgsz'))[:, 1]
# For now, I'm just going to use the existing file, and not include the info I don't have from Aaron's file
#######################
import sys, readtxtfile, cPickle, glob, os, re
import astropy.io.ascii as asciireader
import numpy as np
import readMXXLProfile as rmp
######################
snapnum = int(sys.argv[1])
outfile = sys.argv[2]
redshift = readtxtfile.readtxtfile(
'/vol/euclid1/euclid1_raid1/dapple/mxxl_lensing/mxxlsnap%d/redshift' %
snapnum)[0, 0]
clusterinfo = {}
for halofile in glob.glob(
'/vol/euclid1/euclid1_raid1/dapple/mxxl_lensing/mxxlsnap%d/*.convergence_map'
% snapnum):
halobase = os.path.basename(halofile)
match = re.match('halo_%d_((\d+)_\d)\.convergence_map' % snapnum, halobase)
myid = match.group(1)
stefan_id = int(match.group(2))
#######################
# This file compiles the truth tables for BCC clusters
#######################
import h5py, h5pyutils, sys, os, glob, cPickle
import astropy.io.fits as pyfits
import readtxtfile, ldac
#######################
clusterlist = sys.argv[1]
outfile = sys.argv[2]
clusterfiles = [x[0] for x in readtxtfile.readtxtfile(clusterlist)]
clusterstofind = {}
clusterinfo = {}
for i, clusterfile in enumerate(clusterfiles):
root, ext = os.path.splitext(clusterfile)
clusterid = float(root.split('_')[1])
clusterstofind[clusterid] = None
for halofile in glob.glob('/users/dapple/localdisk/bcc_rawcatalogs/halos/*.fit'):
halocat = ldac.LDACCat(pyfits.open(halofile)[1])
def multibinresidual(binbase, fig = None):
matplotlib.rcParams['figure.figsize'] = [16,16]
if fig is None:
fig = pylab.figure()
curplot = 1
for curm in range(4):
for curc in range(4):
pylab.subplot(4,4,curplot)
colori = 0
# for curz in range(3):
for curz in [0]:
mass, concen, redshift = readtxtfile.readtxtfile('%s_%d_%d_%d.dat' % (binbase, curz, curm, curc))[0]
cat = ldac.openObjectFile('%s_%d_%d_%d.cat' % (binbase, curz, curm, curc))
zlens = cat.hdu.header['ZLENS']
rscale = nfwutils.rscaleConstM(mass/nfwutils.global_cosmology.h, concen, zlens, 200)
gamma = nfwmodeltools.NFWShear(cat['r_mpc'], concen, rscale, zlens)
kappa = nfwmodeltools.NFWKappa(cat['r_mpc'], concen, rscale, zlens)
gpred = cat['beta_s']*gamma / (1 - (cat['beta_s2']*kappa/cat['beta_s']))
pylab.errorbar(cat['r_mpc']*0.72, cat['ghat'], cat['ghatdistrosigma']/(np.sqrt(cat['ndat'])),
linestyle='None', marker='o', color=c[colori], label='BCC z=%1.1f' % redshift)
pylab.plot(cat['r_mpc']*0.72, gpred, 'k-', linewidth=2)
ax = pylab.gca()
ax.set_xscale('log')
pylab.axhline(0.0, c='k', linewidth=2)
pylab.legend(loc='lower center', fontsize=10)
pylab.axis([0.05, 10, -.55, 0.35])
# pylab.axis([0.05, 10, -.10, 0.05])
colori+= 1
pylab.title('M=%1.1fx10^14 C=%1.1f' % ( mass/1e14, concen))
curplot += 1
for i in range(4):
pylab.subplot(4,4,13+i)
pylab.xlabel('Radius [Mpc/h]')
pylab.subplot(4,4,4*i+1)
# pylab.ylabel('<g_m-g_p>/g_p(<M>,<c>)')
# pylab.ylabel('<g_m/g_p - 1>')
pylab.tight_layout()
# pylab.savefig('%s_multibin_fracresid.png' % binbase)
pylab.savefig('%s_multibin_resid.png' % binbase)
return fig
def MXXLmultibinresidualoverplot(binbase, fig = None):
setMXXL()
if fig is None:
matplotlib.rcParams['figure.figsize'] = [16,8]
fig = pylab.figure()
curplot = 5
for curc in range(4):
pylab.subplot(2,4,curplot)
colori = 0
for curm in range(2):
try:
mass, concen = readtxtfile.readtxtfile('%s_%d_%d.dat' % (binbase, curm, curc))[0]
cat = ldac.openObjectFile('%s_%d_%d.cat' % (binbase, curm, curc))
zlens = cat.hdu.header['ZLENS']
rscale = nfwutils.rscaleConstM(mass/nfwutils.global_cosmology.h, concen, zlens, 200)
gamma = nfwmodeltools.NFWShear(cat['r_mpc'], concen, rscale, zlens)
kappa = nfwmodeltools.NFWKappa(cat['r_mpc'], concen, rscale, zlens)
gpred = cat['beta_s']*gamma / (1 - (cat['beta_s2']*kappa/cat['beta_s']))
pylab.errorbar(cat['r_mpc']*nfwutils.global_cosmology.h, cat['ghat'], cat['ghatdistrosigma']/(np.sqrt(cat['ndat'])),
linestyle='None', marker='o', color=c[colori], label='M=%1.1fx10^14' % (mass/1e14))
pylab.plot(cat['r_mpc']*nfwutils.global_cosmology.h, gpred, 'k-', linewidth=2)
ax = pylab.gca()
ax.set_xscale('log')
pylab.axhline(0.0, c='k', linewidth=2)
pylab.axis([0.05, 10, -0.03, 0.4])
except:
pass
colori+= 1
pylab.title('C=%1.1f' % (concen))
curplot += 1
for i in range(4):
pylab.subplot(2,4,4+i+1)
pylab.xlabel('Radius [Mpc/h]')
pylab.text(0.1, 0.03, 'MXXL')
pylab.minorticks_on()
pylab.subplot(2,4,5)
pylab.ylabel('<g_m/g_p-1>')
pylab.subplot(2,4,8)
pylab.legend(loc='upper right')
pylab.tight_layout()
pylab.savefig('%s_shearstack.png' % binbase)
return fig
