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MLmasstable.py
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MLmasstable.py
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#!/usr/bin/env python
########################
#
# Output a latex format table (not including headers, just body)
# compiling best fit information for each cluster
#
########################
import numpy as np, pymc
import compare_masses as cm
import nfwutils, shearprofile as sp
########################
class MLCluster(object):
def __init__(self, cluster, redshift, db, burn):
self.cluster = cluster
self.redshift = redshift
self.db = db
self.burn = burn
#######
def getVar(self, var, *args, **kwds):
return sp.ConfidenceRegion(self.db.trace(var, -1)[self.burn:], *args, **kwds)
#######
def getRs(self):
return self.getVar('r_scale')
#######
def getConcentration(self):
return self.getVar('concentration')
#######
def getMass(self):
return self.getVar('mass_15mpc', useMedian = True)
#######
@classmethod
def load(cls, cluster, filter, image, redshift, workdir = '/u/ki/dapple/subaru/doug/publication/baseline_2011-12-14'):
return cls(cluster, redshift, pymc.database.pickle.load('%s/%s.%s.%s.out' % (workdir, cluster, filter, image)), 10000)
###########################
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 normalStrInterval(bounds, fmt = '%2.2f', norm=1.):
template = '$%s^{+%s}_{-%s}$' % (fmt, fmt, fmt)
mid, (dn, up) = bounds
return template % (mid/norm, up/norm, dn/norm)
#####
def redactedStrInterval(bounds, fmt = '%2.2f', norm=1.):
template = '$%s^{+%s}_{-%s}$' % ('%s', fmt, fmt)
mid, (dn, up) = bounds
return template % ('X', up/norm, dn/norm)
############################################
def readNameTranslation():
translation = {}
with open('/u/ki/dapple/subaru/SUBARU.list') as input:
for line in input.readlines():
tokens = line.split()
translation[tokens[0]] = tokens[1]
with open('/u/ki/anja/MACS/macs_othernames') as input:
for line in input.readlines():
tokens = line.split()
translation[tokens[0]] = tokens[1]
return translation
#############################################
def readFilterTranslation():
return { 'W-J-V' : '{\it V}$_{\rm J}$',
'W-C-RC' : '{\it R}$_{\rm C}$',
'W-C-IC' : '{\it I}$_{\rm C}$',
'W-S-I+' : '{\it i}$^{+}$',
'g' : '{\it g}$^{\star}$',
'r' : '{\it r}$^{\star}$'}
#############################################
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)