def __call__(self, profile):
#only want to define a scan for a 1d model at this point.
assert(isinstance(self.model, NFW_MC_Model))
self.model.setData(profile.beta_s, profile.beta_s2, profile.zcluster, zlens = profile.zlens)
pdfs = {}
masses = self.masses
for delta in self.deltas:
logprob = np.zeros(len(masses))
if delta == 200:
workingmasses = masses
c200s = np.array([self.model.massconRelation(np.abs(curm)*nfwutils.global_cosmology.h,
profile.zcluster, float(delta)) for curm in workingmasses])
elif delta != 200:
workingmasses = np.zeros_like(masses)
c200s = np.zeros_like(masses)
for i, curm in enumerate(masses):
c200 = self.model.massconRelation(np.abs(curm)*nfwutils.global_cosmology.h,
profile.zcluster, float(delta))
rscale = nfwutils.rscaleConstM(np.abs(curm), c200, profile.zcluster, float(delta))
m200 = nfwutils.Mdelta(rscale, c200, profile.zcluster, 200)
if curm < 0:
m200 = -m200
workingmasses[i] = m200
c200s[i] = c200
for i in range(len(workingmasses)):
mass = workingmasses[i]
c200 = c200s[i]
logprob[i] = tools.shearprofile_like(mass, c200,
profile.r_mpc,
profile.ghat,
profile.sigma_ghat,
self.model.beta_s,
self.model.beta_s2,
self.model.rho_c,
self.model.rho_c_over_sigma_c,
200.)
pdf = np.exp(logprob - np.max(logprob))
pdf = pdf/scipy.integrate.trapz(pdf, masses)
pdfs[delta] = pdf
if np.any(np.logical_not(np.isfinite(pdf))):
raise BadPDFException
return (masses, pdfs)
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 reducedShear(scaledm200):
m200 = 1e14 * scaledm200
c = duffy(m200, zcluster)
rs = nfwutils.rscaleConstM(m200, c, zcluster, 200)
gamma = tools.NFWShear(r_mpc, c, rs, rho_c_over_sigma_c)
kappa = tools.NFWKappa(r_mpc, c, rs, rho_c_over_sigma_c)
g = beta_s * gamma / (1 - beta_cor * kappa)
return g
def createPerfectProfile(m200, c, zcluster, r_mpc, beta_s):
rho_c_over_sigma_c = 1.5 * nfwutils.global_cosmology.angulardist(
zcluster) * nfwutils.global_cosmology.beta(
[1e6], zcluster)[0] * nfwutils.global_cosmology.hubble2(
zcluster) / nfwutils.global_cosmology.v_c**2
r_scale = nfwutils.rscaleConstM(m200, c, zcluster, 200)
nfw_shear_inf = tools.NFWShear(r_mpc, c, r_scale, rho_c_over_sigma_c)
nfw_kappa_inf = tools.NFWKappa(r_mpc, c, r_scale, rho_c_over_sigma_c)
g = beta_s * nfw_shear_inf / (1 - (beta_s * nfw_kappa_inf))
return g
def stackResiduals(sims, outdir):
configfile = '%s/config.sh' % outdir
config = nfwfit_bcc.readConfiguration(configfile)
fitter = nfwfit_bcc.buildFitter(config)
radial_points = []
amp_points = []
for simfile in sims:
base = os.path.basename(simfile)
root, ext = os.path.splitext(base)
outfile = '%s/%s.out' % (outdir, root)
simcat = nfwfit_bcc.readSimCatalog(simfile, config)
r_mpc, ghat, sigma_ghat = fitter.profileBuilder(simcat, config)
m200s, nfails = cPickle.load(open(outfile))
fitM200 = np.median(m200s)
trueM200 = simcat.hdu.header['M200C']
zcluster = simcat.hdu.header['ZLENS']
concentration = fitter.massconRelation(trueM200, zcluster, 200)
r_scale = nfwutils.rscaleConstM(trueM200, concentration, zcluster, 200)
profile_g = profileFromMass(fitter.massconRelation, fitM200, 200,
r_mpc, simcat)
# residuals = profile_g - ghat
# scaled_r = r_mpc / r_scale
ratio = profile_g / ghat
# scaled_residuals = residuals / (r_scale*nfwutils.deltaC(concentration)*nfwutils.global_cosmology.angulardist(zcluster) * nfwutils.global_cosmology.beta([1e6], zcluster) * nfwutils.global_cosmology.hubble2(zcluster))
radial_points.extend(r_mpc)
amp_points.extend(ratio)
return radial_points, amp_points
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 profileFromMass(mcrelation, mdelta, delta, r_mpc, simcat):
zcluster = simcat.hdu.header['ZLENS']
concentration = mcrelation(mdelta, zcluster, delta)
r_scale = nfwutils.rscaleConstM(mdelta, concentration, zcluster, delta)
nfw_shear_inf = tools.NFWShear(r_mpc, concentration, r_scale, zcluster)
nfw_kappa_inf = tools.NFWKappa(r_mpc, concentration, r_scale, zcluster)
beta_s = np.mean(simcat['beta_s'])
beta_s2 = np.mean(simcat['beta_s']**2)
g = beta_s * nfw_shear_inf / (1 - ((beta_s2 / beta_s) * nfw_kappa_inf))
return g
def __call__(self, x, m200, c200):
if m200 == 0.:
return np.zeros_like(x)
isNegative=m200 < 0
if isNegative:
m200 = np.abs(m200)
r_scale = nfwutils.rscaleConstM(m200*self.massScale, c200, self.zcluster, self.overdensity)
nfw_shear_inf = tools.NFWShear(x, c200, r_scale, self.rho_c_over_sigma_c)
nfw_kappa_inf = tools.NFWKappa(x, c200, r_scale, self.rho_c_over_sigma_c)
if isNegative:
nfw_shear_inf = -nfw_shear_inf
g = self.beta_s*nfw_shear_inf / (1 - ((self.beta_s2/self.beta_s)*nfw_kappa_inf) )
return g
def __init__(self, filebase):
print 'Loading %s' % filebase
#the file in this case is a configuration file with information about mass, concentration, redshift, etc
super(AnalyticSim, self).__init__()
with open(filebase) as input:
config = yaml.load(input)
max_dist = config['max_dist']
gridlength = config['gridlength']
zcluster = config['zcluster']
m200 = config['m200']
c200 = config['c200']
rscale = nfwutils.rscaleConstM(m200, c200, zcluster, 200.)
xs = np.linspace(-max_dist, max_dist, gridlength)
ys = np.linspace(-max_dist, max_dist, gridlength)
x_mpc, y_mpc = [grid.flatten() for grid in np.meshgrid(xs, ys, indexing='ij')]
r_mpc = np.sqrt(x_mpc**2 + y_mpc**2)
Dl = nfwutils.global_cosmology.angulardist(zcluster)
x_arcmin = (x_mpc/Dl)*(180./np.pi)*60.
y_arcmin = (y_mpc/Dl)*(180./np.pi)*60.
rho_c_over_sigma_c = 1.5 * nfwutils.global_cosmology.angulardist(zcluster) * nfwutils.global_cosmology.beta([1e6], zcluster)[0] * nfwutils.global_cosmology.hubble2(zcluster) / nfwutils.global_cosmology.v_c**2
gamma_t_inf = nfwmodeltools.NFWShear(r_mpc,
c200,
rscale,
rho_c_over_sigma_c)
kappa_inf = nfwmodeltools.NFWKappa(r_mpc,
c200,
rscale,
rho_c_over_sigma_c)
posangle = np.arctan2(y_mpc, x_mpc)
cos2phi = np.cos(2*posangle)
sin2phi = np.sin(2*posangle)
gamma1_inf = -gamma_t_inf*cos2phi
gamma2_inf = -gamma_t_inf*sin2phi
self.zcluster = zcluster
self.x_mpc = x_mpc
self.y_mpc = y_mpc
self.x_arcmin = x_arcmin
self.y_arcmin = y_arcmin
self.gamma1_inf = gamma1_inf
self.gamma2_inf = gamma2_inf
self.kappa_inf = kappa_inf
def consolidateFits(workdir, simtype, outdir):
failfile = open('{0}/fails'.format(outdir), 'w')
idpattern = idpatterns[simtype]
answers = cPickle.load(
open('{0}/{1}_answers.pkl'.format(workdir, simtype), 'rb'))
outputfiles = glob.glob('%s/*.out' % outdir)
nhalos = len(outputfiles)
ids = []
measured_m200s = np.zeros(nhalos)
measured_m200errs = np.zeros(nhalos)
measured_m500s = np.zeros(nhalos)
measured_m500errs = np.zeros(nhalos)
measured_cs = np.zeros(nhalos)
measured_rs = np.zeros(nhalos)
true_m200s = np.zeros(nhalos)
true_m500s = np.zeros(nhalos)
true_cs = np.zeros(nhalos)
redshifts = np.zeros(nhalos)
results = dict(ids=ids,
measured_m200s=measured_m200s,
measured_m200errs=measured_m200errs,
measured_m500s=measured_m500s,
measured_m500errss=measured_m500errs,
measured_cs=measured_cs,
measured_rs=measured_rs,
true_m200s=true_m200s,
true_m500s=true_m500s,
true_cs=true_cs,
redshifts=redshifts)
class WeirdException(Exception):
pass
#load up the environment for cosmology, and mc relation if used
config = nfwfit.readConfiguration('{0}/config.sh'.format(outdir))
simreader = nfwfit.buildSimReader(config)
nfwutils.global_cosmology.set_cosmology(simreader.getCosmology())
fitter = nfwfit.buildFitter(config)
configname = os.path.basename(outdir)
for i, output in enumerate(outputfiles):
filebase = os.path.basename(output)
match = idpattern.match(filebase)
try:
haloid = int(match.group(1))
except AttributeError as e:
print filebase
raise e
except ValueError:
haloid = match.group(1)
try:
truth = answers[haloid]
except KeyError:
print 'Failure at {0}'.format(output)
raise
ids.append(haloid)
true_m200s[i] = truth['m200']
true_m500s[i] = truth['m500']
true_cs[i] = truth['concen']
redshifts[i] = truth['redshift']
input = open(output)
measured = cPickle.load(input)
input.close()
if measured is None:
print 'Fail {0} {1}'.format(configname, haloid)
failfile.write('Fail {0} {1}\n'.format(configname, haloid))
continue
measured_m200s[i] = measured[0][
'm200'] * fitter.model.massScale * nfwutils.global_cosmology.h
measured_m200errs[i] = np.mean(np.abs(
measured[1]
['m200'])) * fitter.model.massScale * nfwutils.global_cosmology.h
if 'c200' in measured:
measured_cs[i] = measured['c200']
else:
## need to dig up the mc relation
measured_cs[i] = fitter.model.massconRelation(
np.abs(measured_m200s[i]), redshifts[i],
fitter.model.overdensity)
#####
#calculate m500
measured_rs[i] = nfwutils.rscaleConstM(np.abs(measured_m200s[i]),
measured_cs[i], redshifts[i],
fitter.model.overdensity)
measured_m500s[i] = nfwutils.Mdelta(measured_rs[i], measured_cs[i],
redshifts[i], 500)
if measured_m200s[i] < 0:
measured_m500s[i] = -measured_m500s[i]
if not np.isfinite(measured_m500s[i]):
print 'NOT FINITE'
print haloid
print measured
cPickle.dump(results, open('%s/consolidated.pkl' % outdir, 'w'))
failfile.close()
#
# simdat1 = data['c4'][radrange]
# simdat2 = data['cfree'][radrange]
#
# massbin1, ratiobin1, ratioerr1 = bbb.summary2DMass(simdat1)
# massbin2, ratiobin2, ratioerr2 = bbb.summary2DMass(simdat2)
#
# massbins2.append(massbin1)
# offset.append((np.array(ratiobin1)/ np.array(ratiobin2)) - 1.)
#
##
figure()
mlow500 = nfwutils.Mdelta(
nfwutils.rscaleConstM(10**massbins[0][0], 4.0, 1.0, 200), 4.0, 1.0,
500) / 1e14
mid500 = nfwutils.Mdelta(
nfwutils.rscaleConstM(10**massbins[0][8], 4.0, 1.0, 200), 4.0, 1.0,
500) / 1e14
mhigh500 = nfwutils.Mdelta(
nfwutils.rscaleConstM(10**massbins[0][-1], 4.0, 1.0, 200), 4.0, 1.0,
500) / 1e14
plot(outerrange[:-1],
100 * np.array([scatter[8] for scatter in scatters[:-1]]),
c='k',
linewidth=1.8,
label='Intrinsic Scatter')
twincolor = '#B366B3'
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
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 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
