def mockTable(parser):
(options, args) = parser.parse_args()
cmdline = '%python mockTable.py ' + args[0]
if len(args) == 0:
parser.print_help()
return
#Open savefile for flat fit and samples
savefile = open(_FLATFIT, 'rb')
flatparams = pickle.load(savefile)
savefile.close()
savefile = open(_FLATSAMPLES, 'rb')
flatsamples = pickle.load(savefile)
savefile.close()
#Open savefile for all mocks' fit and samples
mockparams = []
mocksamples = []
for ii in range(_NMOCKS):
savefile = open(_MOCKFIT[ii], 'rb')
mockparams.append(pickle.load(savefile))
savefile.close()
savefile = open(_MOCKSAMPLES[ii], 'rb')
mocksamples.append(pickle.load(savefile))
savefile.close()
#Set up sections
names = [
'$V_c(R_0)\ [\mathrm{km\ s}^{-1}]$', '$R_0\ [\mathrm{kpc}]$',
'$V_{R,\odot}\ [\mathrm{km\ s}^{-1}]$',
'$\Omega_{\odot}\ [\mathrm{km\ s}^{-1}\ \mathrm{kpc}^{-1}]$',
'$\\sigma_R(R_0)\ [\mathrm{km\ s}^{-1}]$', '$R_0/h_\sigma$',
'$X^2 \equiv \sigma_\phi^2 / \sigma_R^2$',
'$\Delta\chi^2/\mathrm{dof}$'
]
scale = [_REFV0, _REFR0, _VRSUN, 1., _REFV0, 1., 1., 1.]
flatbestfits = []
flatxs = []
mockbestfits = []
mockxs = []
#V_c
flatbestfits.append(flatparams[0])
flatxs.append(numpy.array([s[0] for s in flatsamples]))
#R_0
flatbestfits.append(flatparams[1])
flatxs.append(numpy.array([s[1] for s in flatsamples]))
#VRsun
flatbestfits.append(flatparams[6])
flatxs.append(numpy.array([s[6] for s in flatsamples]))
#Omega_sun
flatbestfits.append(flatparams[7] * _PMSGRA)
flatxs.append(numpy.array([s[7] * _PMSGRA for s in flatsamples]))
#\sigma_R(R_0)
flatbestfits.append(numpy.exp(flatparams[2]))
flatxs.append(numpy.exp(numpy.array([s[2] for s in flatsamples])))
#R_0/h_sigma
flatbestfits.append(flatparams[1] * flatparams[9])
flatxs.append(numpy.array([s[1] * s[9] for s in flatsamples]))
#X^2
flatbestfits.append(flatparams[8])
flatxs.append(numpy.array([s[8] for s in flatsamples]))
#chi2
options = set_options(None)
data = readVclosData(validfeh=True)
options.isofile = '../fits/isos.dat'
thislogl = logl.logl(init=flatparams, options=options, data=data)
flatbestfits.append(-2. * numpy.sum(thislogl) /
(len(data) - len(flatparams)))
if _INDIVCHI2:
locs = numpy.array(sorted(list(set(data['LOCATION']))))
nlocs = len(locs)
platel = numpy.zeros(nlocs)
for ii in range(nlocs):
indx = (data['LOCATION'] == locs[ii])
platel[ii] = numpy.mean(data['GLON'][indx])
sortindx = numpy.argsort(platel)
locs = locs[sortindx]
platel = platel[sortindx]
for jj in range(len(locs)):
names.append(
'$\Delta\chi^2/\mathrm{dof}\ \mathrm{w/o}\ l=%i^\circ$' %
int(round(platel[jj])))
scale.append(1.)
indx = (data['LOCATION'] != locs[jj])
flatbestfits.append(-2. * numpy.sum(thislogl[indx]) /
(len(data[indx]) - len(flatparams)))
else:
locs = []
##Same for all mocks
for ii in range(_NMOCKS):
thisbestfits = []
thisxs = []
#V_c
thisbestfits.append(mockparams[ii][0])
thisxs.append(numpy.array([s[0] for s in mocksamples[ii]]))
#R_0
thisbestfits.append(mockparams[ii][1])
thisxs.append(numpy.array([s[1] for s in mocksamples[ii]]))
#VRsun
thisbestfits.append(mockparams[ii][6])
thisxs.append(numpy.array([s[6] for s in mocksamples[ii]]))
#Omega_sun
thisbestfits.append(mockparams[ii][7] * _PMSGRA)
thisxs.append(numpy.array([s[7] * _PMSGRA for s in mocksamples[ii]]))
#\sigma_R(R_0)
thisbestfits.append(numpy.exp(mockparams[ii][2]))
thisxs.append(numpy.exp(numpy.array([s[2] for s in mocksamples[ii]])))
#R_0/h_sigma
thisbestfits.append(mockparams[ii][1] * mockparams[ii][9])
thisxs.append(numpy.array([s[1] * s[9] for s in mocksamples[ii]]))
#X^2
thisbestfits.append(mockparams[ii][8])
thisxs.append(numpy.array([s[8] for s in mocksamples[ii]]))
#chi2
#Load mock data
data = readVclosData(datafilename=_MOCKDATA[ii])
thislogl = logl.logl(init=mockparams[ii], data=data, options=options)
thisbestfits.append(-2. * numpy.sum(thislogl) /
(len(data) - len(mockparams[ii])))
if _INDIVCHI2:
for jj in range(len(locs)):
indx = (data['LOCATION'] != locs[jj])
thisbestfits.append(-2. * numpy.sum(thislogl[indx]) /
(len(data[indx]) - len(mockparams)))
#Append all
mockbestfits.append(thisbestfits)
mockxs.append(thisxs)
#Make table
quantile = 0.68
outfile = open(args[0], 'w')
for ii in range(len(names)):
#Set up line
printline = names[ii]
#Flat
printline += ' & '
if ii >= len(names) - (len(locs) + 1): #chi2
printline += ' \ldots & '
else:
bestfit = flatbestfits[ii] * scale[ii]
xs = flatxs[ii] * scale[ii]
lowval, highval = sixtyeigthinterval(bestfit,
xs,
quantile=quantile)
dlow, dhigh = bestfit - lowval, highval - bestfit
#Prepare
maxerr = numpy.amin(numpy.fabs([dlow, dhigh]))
print names[ii], maxerr
if math.log10(maxerr) >= 0.:
value = '$%.0f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 1.:
err = '$\pm$%.0f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.0f}_{-%.0f}$' % (dhigh, dlow)
elif math.log10(maxerr) >= -1.:
value = '$%.1f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.1:
err = '$\pm$%.1f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.1f}_{-%.1f}$' % (dhigh, dlow)
elif math.log10(maxerr) >= -2.:
value = '$%.2f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.01:
err = '$\pm$%.2f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.2f}_{-%.2f}$' % (dhigh, dlow)
elif math.log10(maxerr) >= -3.:
value = '$%.3f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.001:
err = '$\pm$%.3f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.3f}_{-%.3f}$' % (dhigh, dlow)
else:
value = '$%.4f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.0001:
err = '$\pm$%.4f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.4f}_{-%.4f}$' % (dhigh, dlow)
printline += value + '&' + err
#Mocks
for jj in range(_NMOCKS):
printline += ' & '
if ii >= len(names) - (len(locs) + 1): #chi2
bestfit = mockbestfits[jj][ii] * scale[ii] - flatbestfits[
ii] * scale[ii]
printline += '$%.2f$ & ' % bestfit
if jj == _NMOCKS - 1:
if not ii == (len(names) - 1):
printline += '\\\\'
outfile.write(printline + '\n')
continue
bestfit = mockbestfits[jj][ii] * scale[ii]
xs = mockxs[jj][ii] * scale[ii]
lowval, highval = sixtyeigthinterval(bestfit,
xs,
quantile=quantile)
dlow, dhigh = bestfit - lowval, highval - bestfit
#Prepare
maxerr = numpy.amin(numpy.fabs([dlow, dhigh]))
if math.log10(maxerr) >= 0.:
value = '$%.0f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 1.:
err = '$\pm$%.0f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.0f}_{-%.0f}$' % (dhigh, dlow)
elif math.log10(maxerr) >= -1.:
value = '$%.1f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.1:
err = '$\pm$%.1f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.1f}_{-%.1f}$' % (dhigh, dlow)
elif math.log10(maxerr) >= -2.:
value = '$%.2f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.01:
err = '$\pm$%.2f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.2f}_{-%.2f}$' % (dhigh, dlow)
elif math.log10(maxerr) >= -3.:
value = '$%.3f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.001:
err = '$\pm$%.3f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.3f}_{-%.3f}$' % (dhigh, dlow)
else:
value = '$%.4f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.0001:
err = '$\pm$%.4f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.4f}_{-%.4f}$' % (dhigh, dlow)
printline += value + '&' + err
if not ii == (len(names) - 1):
printline += '\\\\'
if ii == (len(names) - (len(locs) + 2)):
printline += '\\\\'
if ii < (len(names) - (len(locs) + 1)):
outfile.write(printline + '\n')
outfile.write('\\enddata\n')
outfile.write(cmdline + '\n')
outfile.close()
def fitMass():
#Read data
vcircdata= numpy.loadtxt('vcirc.txt',comments='#',delimiter='|')
vcircdata[:,0]/= _REFR0
vcircdata[:,1]/= _REFV0
vcircdata[:,2]/= _REFV0
#Set-up
bp= HernquistPotential(a=0.6/_REFR0,normalize=1./3.)
if _dexp:
dp= DoubleExponentialDiskPotential(normalize=1./3.,
hr=3.25/_REFR0,
hz=0.3/_REFR0)
else:
dp= MiyamotoNagaiPotential(normalize=1./3.,
a=3.25/_REFR0,
b=0.3/_REFR0)
hp= NFWPotential(normalize=1./3.,
a=5./_REFR0)
init= numpy.array([0.6,0.35,218./_REFV0,15./_REFR0,3.25/_REFR0])
#print _convertrho
out= optimize.fmin_powell(obj,init,args=(vcircdata,bp,dp,hp),
callback=cb)
print out
#Calculate mass
#Halo mass
halo= halomass(out)
bulge= halomass(out)
disk= diskmass(out)
print halo, disk, bulge, totalmass(out)
#Sample
samples= bovy_mcmc.markovpy(out,0.05,
(lambda x: -obj(x,vcircdata,bp,dp,hp)),
(),
isDomainFinite=[[True,True],
[True,True],
[True,True],
[True,True],
[True,True]],
domain=[[0.,1.],
[0.,1.],
[0.,2.],
[0.,10.],
[0.,2.]],
nwalkers=8,
nsamples=10000)
print "Done with sampling ..."
print numpy.mean(numpy.array(samples),axis=0)
print numpy.std(numpy.array(samples),axis=0)
samples= numpy.random.permutation(samples)[0:500]
#halo
totalmasssamples= []
for s in samples:
totalmasssamples.append(halomass(s))
totalmasssamples= numpy.array(totalmasssamples)
print "halo mass: ", numpy.mean(totalmasssamples), numpy.std(totalmasssamples)
print sixtyeigthinterval(halomass(out),totalmasssamples,quantile=.68)
#total
totalmasssamples= []
for s in samples:
totalmasssamples.append(totalmass(s))
totalmasssamples= numpy.array(totalmasssamples)
print "total mass: ", numpy.mean(totalmasssamples), numpy.std(totalmasssamples)
print sixtyeigthinterval(totalmass(out),totalmasssamples,quantile=.68)
bovy_plot.bovy_print()
bovy_plot.bovy_hist(totalmasssamples,bins=16,range=[0.,2.])
bovy_plot.bovy_end_print('totalmass.png')
return None
#disk
totalmasssamples= []
for s in samples:
totalmasssamples.append(diskmass(s))
totalmasssamples= numpy.array(totalmasssamples)
print "disk mass: ", numpy.mean(totalmasssamples), numpy.std(totalmasssamples)
#bulge
totalmasssamples= []
for s in samples:
totalmasssamples.append(bulgemass(s))
totalmasssamples= numpy.array(totalmasssamples)
print "bulge mass: ", numpy.mean(totalmasssamples), numpy.std(totalmasssamples)
return None
def mockNonFlatTable(parser):
(options, args) = parser.parse_args()
cmdline = '%python mockNonFlatTable.py ' + args[0]
if len(args) == 0:
parser.print_help()
return
#Open savefile for flat fit and samples
savefile = open(_FLATFIT, 'rb')
flatparams = pickle.load(savefile)
savefile.close()
savefile = open(_FLATSAMPLES, 'rb')
flatsamples = pickle.load(savefile)
savefile.close()
#Set up sections
names = [
'$V_c(R_0)\ [\mathrm{km\ s}^{-1}]$', '$\\beta$',
'$R_0\ [\mathrm{kpc}]$', '$V_{R,\odot}\ [\mathrm{km\ s}^{-1}]$',
'$\Omega_{\odot}\ [\mathrm{km\ s}^{-1}\ \mathrm{kpc}^{-1}]$',
'$\\sigma_R(R_0)\ [\mathrm{km\ s}^{-1}]$', '$R_0/h_\sigma$',
'$X^2 \equiv \sigma_\phi^2 / \sigma_R^2$'
]
missing = [0, 1, 0, 0, 0, 0, 0, 0] #1 if missing for flat
scale = [_REFV0, 1., _REFR0, _VRSUN, 1., _REFV0, 1., 1.]
flatbestfits = []
flatxs = []
#V_c
flatbestfits.append(flatparams[0])
flatxs.append(numpy.array([s[0] for s in flatsamples]))
#beta
flatbestfits.append(None)
flatxs.append(None)
#R_0
flatbestfits.append(flatparams[1])
flatxs.append(numpy.array([s[1] for s in flatsamples]))
#VRsun
flatbestfits.append(flatparams[6])
flatxs.append(numpy.array([s[6] for s in flatsamples]))
#Omega_sun
flatbestfits.append(flatparams[7] * _PMSGRA)
flatxs.append(numpy.array([s[7] * _PMSGRA for s in flatsamples]))
#\sigma_R(R_0)
flatbestfits.append(numpy.exp(flatparams[2]))
flatxs.append(numpy.exp(numpy.array([s[2] for s in flatsamples])))
#R_0/h_sigma
flatbestfits.append(flatparams[1] * flatparams[9])
flatxs.append(numpy.array([s[1] * s[9] for s in flatsamples]))
#X^2
flatbestfits.append(flatparams[8])
flatxs.append(numpy.array([s[8] for s in flatsamples]))
#Make table
quantile = 0.68
outfile = open(args[0], 'w')
for kk in range(_NSECTIONS):
#Open savefile for all mocks' fit and samples
mockparams = []
mocksamples = []
for ii in range(_NMOCKS):
savefile = open(_MOCKFIT[ii].replace('FLAT', _SECTIONS[kk]), 'rb')
mockparams.append(pickle.load(savefile))
savefile.close()
savefile = open(_MOCKSAMPLES[ii].replace('FLAT', _SECTIONS[kk]),
'rb')
mocksamples.append(pickle.load(savefile))
savefile.close()
mockbestfits = []
mockxs = []
##Same for all mocks
if _SECTIONS[kk] == 'powerlaw' or _SECTIONS[kk] == 'linear':
skip = 1
elif _SECTIONS[kk] == 'quadratic':
skip = 2
elif _SECTIONS[kk] == 'cubic':
skip = 3
if _SECTIONS[kk] == 'linear':
names[
1] = '$\\mathrm{d} V_c / \mathrm{d} R \left(R_0 \\right)\ [\mathrm{km\ s}^{-1}\ \mathrm{kpc}^{-1}]$'
elif _SECTIONS[kk] == 'quadratic':
names.insert(
2,
'$\\mathrm{d}^2 V_c / \mathrm{d} R^2 \left(R_0 \\right)\ [\mathrm{km\ s}^{-1}\ \mathrm{kpc}^{-2}]$'
)
flatbestfits.insert(2, None)
flatxs.insert(2, None)
missing.insert(2, 1)
scale.insert(2, 1.)
elif _SECTIONS[kk] == 'cubic':
if not 'quadratic' in _SECTIONS:
#First insert quadratic
names.insert(
2,
'$\\mathrm{d}^2 V_c / \mathrm{d} R^2 \left(R_0 \\right)\ [\mathrm{km\ s}^{-1}\ \mathrm{kpc}^{-2}]$'
)
flatbestfits.insert(2, None)
flatxs.insert(2, None)
missing.insert(2, 1)
scale.insert(2, 1.)
#Assumes that quadratic was already inserted
names.insert(
3,
'$\\mathrm{d}^3 V_c / \mathrm{d} R^3 \left(R_0 \\right)\ [\mathrm{km\ s}^{-1}\ \mathrm{kpc}^{-3}]$'
)
flatbestfits.insert(3, None)
flatxs.insert(3, None)
missing.insert(3, 1)
scale.insert(3, 1.)
for ii in range(_NMOCKS):
thisbestfits = []
thisxs = []
#V_c
thisbestfits.append(mockparams[ii][0])
thisxs.append(numpy.array([s[0] for s in mocksamples[ii]]))
#Beta
if _SECTIONS[kk] == 'powerlaw':
thisbestfits.append(mockparams[ii][6])
thisxs.append(numpy.array([s[6] for s in mocksamples[ii]]))
elif _SECTIONS[kk] == 'linear' or _SECTIONS[
kk] == 'quadratic' or _SECTIONS[kk] == 'cubic':
thisbestfits.append(mockparams[ii][6] * _REFV0 / _REFR0)
thisxs.append(
numpy.array(
[s[6] * _REFV0 / _REFR0 for s in mocksamples[ii]]))
if _SECTIONS[kk] == 'quadratic' or _SECTIONS[kk] == 'cubic':
thisbestfits.append(mockparams[ii][7] * _REFV0 / _REFR0**2.)
thisxs.append(
numpy.array(
[s[7] * _REFV0 / _REFR0**2. for s in mocksamples[ii]]))
if _SECTIONS[kk] == 'cubic':
thisbestfits.append(mockparams[ii][8] * _REFV0 / _REFR0**3.)
thisxs.append(
numpy.array(
[s[8] * _REFV0 / _REFR0**3. for s in mocksamples[ii]]))
#R_0
thisbestfits.append(mockparams[ii][1])
thisxs.append(numpy.array([s[1] for s in mocksamples[ii]]))
#VRsun
thisbestfits.append(mockparams[ii][6 + skip])
thisxs.append(numpy.array([s[6 + skip] for s in mocksamples[ii]]))
#Omega_sun
thisbestfits.append(mockparams[ii][7 + skip] * _PMSGRA)
thisxs.append(
numpy.array([s[7 + skip] * _PMSGRA for s in mocksamples[ii]]))
#\sigma_R(R_0)
thisbestfits.append(numpy.exp(mockparams[ii][2]))
thisxs.append(
numpy.exp(numpy.array([s[2] for s in mocksamples[ii]])))
#R_0/h_sigma
thisbestfits.append(mockparams[ii][1] * mockparams[ii][9 + skip])
thisxs.append(
numpy.array([s[1] * s[9 + skip] for s in mocksamples[ii]]))
#X^2
thisbestfits.append(mockparams[ii][8 + skip])
thisxs.append(numpy.array([s[8 + skip] for s in mocksamples[ii]]))
#Append all
mockbestfits.append(thisbestfits)
mockxs.append(thisxs)
for ii in range(len(names)):
#Set up line
if ii == 0:
if _SECTIONS[kk] == 'powerlaw':
printline = 'power-law'
else:
printline = _SECTIONS[kk]
else:
printline = " "
printline += ' & '
printline += names[ii]
#Flat
printline += ' & '
if missing[ii]:
printline += '\ldots & '
else:
bestfit = flatbestfits[ii] * scale[ii]
xs = flatxs[ii] * scale[ii]
lowval, highval = sixtyeigthinterval(bestfit,
xs,
quantile=quantile)
dlow, dhigh = bestfit - lowval, highval - bestfit
#Prepare
maxerr = numpy.amin(numpy.fabs([dlow, dhigh]))
if math.log10(maxerr) >= 0.:
value = '$%.0f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 1.:
err = '$\pm$%.0f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.0f}_{-%.0f}$' % (dhigh, dlow)
elif math.log10(maxerr) >= -1.:
value = '$%.1f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.1:
err = '$\pm$%.1f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.1f}_{-%.1f}$' % (dhigh, dlow)
elif math.log10(maxerr) >= -2.:
value = '$%.2f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.01:
err = '$\pm$%.2f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.2f}_{-%.2f}$' % (dhigh, dlow)
elif math.log10(maxerr) >= -3.:
value = '$%.3f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.001:
err = '$\pm$%.3f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.3f}_{-%.3f}$' % (dhigh, dlow)
else:
value = '$%.4f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.0001:
err = '$\pm$%.4f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.4f}_{-%.4f}$' % (dhigh, dlow)
printline += value + '&' + err
#Mocks
for jj in range(_NMOCKS):
printline += ' & '
print _SECTIONS[kk], jj, ii, mockbestfits[jj][ii]
bestfit = mockbestfits[jj][ii] * scale[ii]
xs = mockxs[jj][ii] * scale[ii]
lowval, highval = sixtyeigthinterval(bestfit,
xs,
quantile=quantile)
dlow, dhigh = bestfit - lowval, highval - bestfit
#Prepare
maxerr = numpy.amin(numpy.fabs([dlow, dhigh]))
if math.log10(maxerr) >= 0.:
value = '$%.0f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 1.:
err = '$\pm$%.0f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.0f}_{-%.0f}$' % (dhigh, dlow)
elif math.log10(maxerr) >= -1.:
value = '$%.1f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.1:
err = '$\pm$%.1f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.1f}_{-%.1f}$' % (dhigh, dlow)
elif math.log10(maxerr) >= -2.:
value = '$%.2f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.01:
err = '$\pm$%.2f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.2f}_{-%.2f}$' % (dhigh, dlow)
elif math.log10(maxerr) >= -3.:
value = '$%.3f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.001:
err = '$\pm$%.3f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.3f}_{-%.3f}$' % (dhigh, dlow)
else:
value = '$%.4f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.0001:
err = '$\pm$%.4f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.4f}_{-%.4f}$' % (dhigh, dlow)
printline += value + '&' + err
if not (kk == (_NSECTIONS - 1) and ii == (len(names) - 1)):
printline += '\\\\'
if not kk == (_NSECTIONS - 1) and ii == (len(names) - 1):
printline += '\\\\'
outfile.write(printline + '\n')
outfile.write('\\enddata\n')
outfile.write(cmdline + '\n')
outfile.close()
def mockNonFlatTable(parser):
(options,args)= parser.parse_args()
cmdline= '%python mockNonFlatTable.py '+args[0]
if len(args) == 0:
parser.print_help()
return
#Open savefile for flat fit and samples
savefile= open(_FLATFIT,'rb')
flatparams= pickle.load(savefile)
savefile.close()
savefile= open(_FLATSAMPLES,'rb')
flatsamples= pickle.load(savefile)
savefile.close()
#Set up sections
names= ['$V_c(R_0)\ [\mathrm{km\ s}^{-1}]$',
'$\\beta$',
'$R_0\ [\mathrm{kpc}]$',
'$V_{R,\odot}\ [\mathrm{km\ s}^{-1}]$',
'$\Omega_{\odot}\ [\mathrm{km\ s}^{-1}\ \mathrm{kpc}^{-1}]$',
'$\\sigma_R(R_0)\ [\mathrm{km\ s}^{-1}]$',
'$R_0/h_\sigma$',
'$X^2 \equiv \sigma_\phi^2 / \sigma_R^2$']
missing= [0,1,0,0,0,0,0,0] #1 if missing for flat
scale= [_REFV0,1.,
_REFR0,_VRSUN,1.,_REFV0,1.,1.]
flatbestfits= []
flatxs= []
#V_c
flatbestfits.append(flatparams[0])
flatxs.append(numpy.array([s[0] for s in flatsamples]))
#beta
flatbestfits.append(None)
flatxs.append(None)
#R_0
flatbestfits.append(flatparams[1])
flatxs.append(numpy.array([s[1] for s in flatsamples]))
#VRsun
flatbestfits.append(flatparams[6])
flatxs.append(numpy.array([s[6] for s in flatsamples]))
#Omega_sun
flatbestfits.append(flatparams[7]*_PMSGRA)
flatxs.append(numpy.array([s[7]*_PMSGRA for s in flatsamples]))
#\sigma_R(R_0)
flatbestfits.append(numpy.exp(flatparams[2]))
flatxs.append(numpy.exp(numpy.array([s[2] for s in flatsamples])))
#R_0/h_sigma
flatbestfits.append(flatparams[1]*flatparams[9])
flatxs.append(numpy.array([s[1]*s[9] for s in flatsamples]))
#X^2
flatbestfits.append(flatparams[8])
flatxs.append(numpy.array([s[8] for s in flatsamples]))
#Make table
quantile= 0.68
outfile= open(args[0],'w')
for kk in range(_NSECTIONS):
#Open savefile for all mocks' fit and samples
mockparams= []
mocksamples= []
for ii in range(_NMOCKS):
savefile= open(_MOCKFIT[ii].replace('FLAT',_SECTIONS[kk]),'rb')
mockparams.append(pickle.load(savefile))
savefile.close()
savefile= open(_MOCKSAMPLES[ii].replace('FLAT',_SECTIONS[kk]),'rb')
mocksamples.append(pickle.load(savefile))
savefile.close()
mockbestfits= []
mockxs= []
##Same for all mocks
if _SECTIONS[kk] == 'powerlaw' or _SECTIONS[kk] == 'linear':
skip= 1
elif _SECTIONS[kk] == 'quadratic':
skip= 2
elif _SECTIONS[kk] == 'cubic':
skip= 3
if _SECTIONS[kk] == 'linear':
names[1]= '$\\mathrm{d} V_c / \mathrm{d} R \left(R_0 \\right)\ [\mathrm{km\ s}^{-1}\ \mathrm{kpc}^{-1}]$'
elif _SECTIONS[kk] == 'quadratic':
names.insert(2,'$\\mathrm{d}^2 V_c / \mathrm{d} R^2 \left(R_0 \\right)\ [\mathrm{km\ s}^{-1}\ \mathrm{kpc}^{-2}]$')
flatbestfits.insert(2,None)
flatxs.insert(2,None)
missing.insert(2,1)
scale.insert(2,1.)
elif _SECTIONS[kk] == 'cubic':
if not 'quadratic' in _SECTIONS:
#First insert quadratic
names.insert(2,'$\\mathrm{d}^2 V_c / \mathrm{d} R^2 \left(R_0 \\right)\ [\mathrm{km\ s}^{-1}\ \mathrm{kpc}^{-2}]$')
flatbestfits.insert(2,None)
flatxs.insert(2,None)
missing.insert(2,1)
scale.insert(2,1.)
#Assumes that quadratic was already inserted
names.insert(3,'$\\mathrm{d}^3 V_c / \mathrm{d} R^3 \left(R_0 \\right)\ [\mathrm{km\ s}^{-1}\ \mathrm{kpc}^{-3}]$')
flatbestfits.insert(3,None)
flatxs.insert(3,None)
missing.insert(3,1)
scale.insert(3,1.)
for ii in range(_NMOCKS):
thisbestfits= []
thisxs= []
#V_c
thisbestfits.append(mockparams[ii][0])
thisxs.append(numpy.array([s[0] for s in mocksamples[ii]]))
#Beta
if _SECTIONS[kk] == 'powerlaw':
thisbestfits.append(mockparams[ii][6])
thisxs.append(numpy.array([s[6] for s in mocksamples[ii]]))
elif _SECTIONS[kk] == 'linear' or _SECTIONS[kk] == 'quadratic' or _SECTIONS[kk] == 'cubic':
thisbestfits.append(mockparams[ii][6]*_REFV0/_REFR0)
thisxs.append(numpy.array([s[6]*_REFV0/_REFR0 for s in mocksamples[ii]]))
if _SECTIONS[kk] == 'quadratic' or _SECTIONS[kk] == 'cubic':
thisbestfits.append(mockparams[ii][7]*_REFV0/_REFR0**2.)
thisxs.append(numpy.array([s[7]*_REFV0/_REFR0**2. for s in mocksamples[ii]]))
if _SECTIONS[kk] == 'cubic':
thisbestfits.append(mockparams[ii][8]*_REFV0/_REFR0**3.)
thisxs.append(numpy.array([s[8]*_REFV0/_REFR0**3. for s in mocksamples[ii]]))
#R_0
thisbestfits.append(mockparams[ii][1])
thisxs.append(numpy.array([s[1] for s in mocksamples[ii]]))
#VRsun
thisbestfits.append(mockparams[ii][6+skip])
thisxs.append(numpy.array([s[6+skip] for s in mocksamples[ii]]))
#Omega_sun
thisbestfits.append(mockparams[ii][7+skip]*_PMSGRA)
thisxs.append(numpy.array([s[7+skip]*_PMSGRA for s in mocksamples[ii]]))
#\sigma_R(R_0)
thisbestfits.append(numpy.exp(mockparams[ii][2]))
thisxs.append(numpy.exp(numpy.array([s[2] for s in mocksamples[ii]])))
#R_0/h_sigma
thisbestfits.append(mockparams[ii][1]*mockparams[ii][9+skip])
thisxs.append(numpy.array([s[1]*s[9+skip] for s in mocksamples[ii]]))
#X^2
thisbestfits.append(mockparams[ii][8+skip])
thisxs.append(numpy.array([s[8+skip] for s in mocksamples[ii]]))
#Append all
mockbestfits.append(thisbestfits)
mockxs.append(thisxs)
for ii in range(len(names)):
#Set up line
if ii == 0:
if _SECTIONS[kk] == 'powerlaw':
printline= 'power-law'
else:
printline= _SECTIONS[kk]
else:
printline= " "
printline+= ' & '
printline+= names[ii]
#Flat
printline+= ' & '
if missing[ii]:
printline+= '\ldots & '
else:
bestfit= flatbestfits[ii]*scale[ii]
xs= flatxs[ii]*scale[ii]
lowval, highval= sixtyeigthinterval(bestfit,xs,quantile=quantile)
dlow, dhigh= bestfit-lowval, highval-bestfit
#Prepare
maxerr= numpy.amin(numpy.fabs([dlow,dhigh]))
if math.log10(maxerr) >= 0.:
value= '$%.0f$' % (bestfit)
if numpy.fabs((dlow-dhigh)) < 1.:
err= '$\pm$%.0f' % ((dlow+dhigh)/2.)
else:
err= '$^{+%.0f}_{-%.0f}$' % (dhigh,dlow)
elif math.log10(maxerr) >= -1.:
value= '$%.1f$' % (bestfit)
if numpy.fabs((dlow-dhigh)) < 0.1:
err= '$\pm$%.1f' % ((dlow+dhigh)/2.)
else:
err= '$^{+%.1f}_{-%.1f}$' % (dhigh,dlow)
elif math.log10(maxerr) >= -2.:
value= '$%.2f$' % (bestfit)
if numpy.fabs((dlow-dhigh)) < 0.01:
err= '$\pm$%.2f' % ((dlow+dhigh)/2.)
else:
err= '$^{+%.2f}_{-%.2f}$' % (dhigh,dlow)
elif math.log10(maxerr) >= -3.:
value= '$%.3f$' % (bestfit)
if numpy.fabs((dlow-dhigh)) < 0.001:
err= '$\pm$%.3f' % ((dlow+dhigh)/2.)
else:
err= '$^{+%.3f}_{-%.3f}$' % (dhigh,dlow)
else:
value= '$%.4f$' % (bestfit)
if numpy.fabs((dlow-dhigh)) < 0.0001:
err= '$\pm$%.4f' % ((dlow+dhigh)/2.)
else:
err= '$^{+%.4f}_{-%.4f}$' % (dhigh,dlow)
printline+= value+'&'+err
#Mocks
for jj in range(_NMOCKS):
printline+= ' & '
print _SECTIONS[kk], jj, ii, mockbestfits[jj][ii]
bestfit= mockbestfits[jj][ii]*scale[ii]
xs= mockxs[jj][ii]*scale[ii]
lowval, highval= sixtyeigthinterval(bestfit,xs,quantile=quantile)
dlow, dhigh= bestfit-lowval, highval-bestfit
#Prepare
maxerr= numpy.amin(numpy.fabs([dlow,dhigh]))
if math.log10(maxerr) >= 0.:
value= '$%.0f$' % (bestfit)
if numpy.fabs((dlow-dhigh)) < 1.:
err= '$\pm$%.0f' % ((dlow+dhigh)/2.)
else:
err= '$^{+%.0f}_{-%.0f}$' % (dhigh,dlow)
elif math.log10(maxerr) >= -1.:
value= '$%.1f$' % (bestfit)
if numpy.fabs((dlow-dhigh)) < 0.1:
err= '$\pm$%.1f' % ((dlow+dhigh)/2.)
else:
err= '$^{+%.1f}_{-%.1f}$' % (dhigh,dlow)
elif math.log10(maxerr) >= -2.:
value= '$%.2f$' % (bestfit)
if numpy.fabs((dlow-dhigh)) < 0.01:
err= '$\pm$%.2f' % ((dlow+dhigh)/2.)
else:
err= '$^{+%.2f}_{-%.2f}$' % (dhigh,dlow)
elif math.log10(maxerr) >= -3.:
value= '$%.3f$' % (bestfit)
if numpy.fabs((dlow-dhigh)) < 0.001:
err= '$\pm$%.3f' % ((dlow+dhigh)/2.)
else:
err= '$^{+%.3f}_{-%.3f}$' % (dhigh,dlow)
else:
value= '$%.4f$' % (bestfit)
if numpy.fabs((dlow-dhigh)) < 0.0001:
err= '$\pm$%.4f' % ((dlow+dhigh)/2.)
else:
err= '$^{+%.4f}_{-%.4f}$' % (dhigh,dlow)
printline+= value+'&'+err
if not (kk == (_NSECTIONS-1) and ii == (len(names)-1)):
printline+= '\\\\'
if not kk == (_NSECTIONS-1) and ii == (len(names)-1):
printline+= '\\\\'
outfile.write(printline+'\n')
outfile.write('\\enddata\n')
outfile.write(cmdline+'\n')
outfile.close()
def mockTable(parser):
(options, args) = parser.parse_args()
cmdline = "%python mockTable.py " + args[0]
if len(args) == 0:
parser.print_help()
return
# Open savefile for flat fit and samples
savefile = open(_FLATFIT, "rb")
flatparams = pickle.load(savefile)
savefile.close()
savefile = open(_FLATSAMPLES, "rb")
flatsamples = pickle.load(savefile)
savefile.close()
# Open savefile for all mocks' fit and samples
mockparams = []
mocksamples = []
for ii in range(_NMOCKS):
savefile = open(_MOCKFIT[ii], "rb")
mockparams.append(pickle.load(savefile))
savefile.close()
savefile = open(_MOCKSAMPLES[ii], "rb")
mocksamples.append(pickle.load(savefile))
savefile.close()
# Set up sections
names = [
"$V_c(R_0)\ [\mathrm{km\ s}^{-1}]$",
"$R_0\ [\mathrm{kpc}]$",
"$V_{R,\odot}\ [\mathrm{km\ s}^{-1}]$",
"$\Omega_{\odot}\ [\mathrm{km\ s}^{-1}\ \mathrm{kpc}^{-1}]$",
"$\\sigma_R(R_0)\ [\mathrm{km\ s}^{-1}]$",
"$R_0/h_\sigma$",
"$X^2 \equiv \sigma_\phi^2 / \sigma_R^2$",
"$\Delta\chi^2/\mathrm{dof}$",
]
scale = [_REFV0, _REFR0, _VRSUN, 1.0, _REFV0, 1.0, 1.0, 1.0]
flatbestfits = []
flatxs = []
mockbestfits = []
mockxs = []
# V_c
flatbestfits.append(flatparams[0])
flatxs.append(numpy.array([s[0] for s in flatsamples]))
# R_0
flatbestfits.append(flatparams[1])
flatxs.append(numpy.array([s[1] for s in flatsamples]))
# VRsun
flatbestfits.append(flatparams[6])
flatxs.append(numpy.array([s[6] for s in flatsamples]))
# Omega_sun
flatbestfits.append(flatparams[7] * _PMSGRA)
flatxs.append(numpy.array([s[7] * _PMSGRA for s in flatsamples]))
# \sigma_R(R_0)
flatbestfits.append(numpy.exp(flatparams[2]))
flatxs.append(numpy.exp(numpy.array([s[2] for s in flatsamples])))
# R_0/h_sigma
flatbestfits.append(flatparams[1] * flatparams[9])
flatxs.append(numpy.array([s[1] * s[9] for s in flatsamples]))
# X^2
flatbestfits.append(flatparams[8])
flatxs.append(numpy.array([s[8] for s in flatsamples]))
# chi2
options = set_options(None)
data = readVclosData(validfeh=True)
options.isofile = "../fits/isos.dat"
thislogl = logl.logl(init=flatparams, options=options, data=data)
flatbestfits.append(-2.0 * numpy.sum(thislogl) / (len(data) - len(flatparams)))
if _INDIVCHI2:
locs = numpy.array(sorted(list(set(data["LOCATION"]))))
nlocs = len(locs)
platel = numpy.zeros(nlocs)
for ii in range(nlocs):
indx = data["LOCATION"] == locs[ii]
platel[ii] = numpy.mean(data["GLON"][indx])
sortindx = numpy.argsort(platel)
locs = locs[sortindx]
platel = platel[sortindx]
for jj in range(len(locs)):
names.append("$\Delta\chi^2/\mathrm{dof}\ \mathrm{w/o}\ l=%i^\circ$" % int(round(platel[jj])))
scale.append(1.0)
indx = data["LOCATION"] != locs[jj]
flatbestfits.append(-2.0 * numpy.sum(thislogl[indx]) / (len(data[indx]) - len(flatparams)))
else:
locs = []
##Same for all mocks
for ii in range(_NMOCKS):
thisbestfits = []
thisxs = []
# V_c
thisbestfits.append(mockparams[ii][0])
thisxs.append(numpy.array([s[0] for s in mocksamples[ii]]))
# R_0
thisbestfits.append(mockparams[ii][1])
thisxs.append(numpy.array([s[1] for s in mocksamples[ii]]))
# VRsun
thisbestfits.append(mockparams[ii][6])
thisxs.append(numpy.array([s[6] for s in mocksamples[ii]]))
# Omega_sun
thisbestfits.append(mockparams[ii][7] * _PMSGRA)
thisxs.append(numpy.array([s[7] * _PMSGRA for s in mocksamples[ii]]))
# \sigma_R(R_0)
thisbestfits.append(numpy.exp(mockparams[ii][2]))
thisxs.append(numpy.exp(numpy.array([s[2] for s in mocksamples[ii]])))
# R_0/h_sigma
thisbestfits.append(mockparams[ii][1] * mockparams[ii][9])
thisxs.append(numpy.array([s[1] * s[9] for s in mocksamples[ii]]))
# X^2
thisbestfits.append(mockparams[ii][8])
thisxs.append(numpy.array([s[8] for s in mocksamples[ii]]))
# chi2
# Load mock data
data = readVclosData(datafilename=_MOCKDATA[ii])
thislogl = logl.logl(init=mockparams[ii], data=data, options=options)
thisbestfits.append(-2.0 * numpy.sum(thislogl) / (len(data) - len(mockparams[ii])))
if _INDIVCHI2:
for jj in range(len(locs)):
indx = data["LOCATION"] != locs[jj]
thisbestfits.append(-2.0 * numpy.sum(thislogl[indx]) / (len(data[indx]) - len(mockparams)))
# Append all
mockbestfits.append(thisbestfits)
mockxs.append(thisxs)
# Make table
quantile = 0.68
outfile = open(args[0], "w")
for ii in range(len(names)):
# Set up line
printline = names[ii]
# Flat
printline += " & "
if ii >= len(names) - (len(locs) + 1): # chi2
printline += " \ldots & "
else:
bestfit = flatbestfits[ii] * scale[ii]
xs = flatxs[ii] * scale[ii]
lowval, highval = sixtyeigthinterval(bestfit, xs, quantile=quantile)
dlow, dhigh = bestfit - lowval, highval - bestfit
# Prepare
maxerr = numpy.amin(numpy.fabs([dlow, dhigh]))
print names[ii], maxerr
if math.log10(maxerr) >= 0.0:
value = "$%.0f$" % (bestfit)
if numpy.fabs((dlow - dhigh)) < 1.0:
err = "$\pm$%.0f" % ((dlow + dhigh) / 2.0)
else:
err = "$^{+%.0f}_{-%.0f}$" % (dhigh, dlow)
elif math.log10(maxerr) >= -1.0:
value = "$%.1f$" % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.1:
err = "$\pm$%.1f" % ((dlow + dhigh) / 2.0)
else:
err = "$^{+%.1f}_{-%.1f}$" % (dhigh, dlow)
elif math.log10(maxerr) >= -2.0:
value = "$%.2f$" % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.01:
err = "$\pm$%.2f" % ((dlow + dhigh) / 2.0)
else:
err = "$^{+%.2f}_{-%.2f}$" % (dhigh, dlow)
elif math.log10(maxerr) >= -3.0:
value = "$%.3f$" % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.001:
err = "$\pm$%.3f" % ((dlow + dhigh) / 2.0)
else:
err = "$^{+%.3f}_{-%.3f}$" % (dhigh, dlow)
else:
value = "$%.4f$" % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.0001:
err = "$\pm$%.4f" % ((dlow + dhigh) / 2.0)
else:
err = "$^{+%.4f}_{-%.4f}$" % (dhigh, dlow)
printline += value + "&" + err
# Mocks
for jj in range(_NMOCKS):
printline += " & "
if ii >= len(names) - (len(locs) + 1): # chi2
bestfit = mockbestfits[jj][ii] * scale[ii] - flatbestfits[ii] * scale[ii]
printline += "$%.2f$ & " % bestfit
if jj == _NMOCKS - 1:
if not ii == (len(names) - 1):
printline += "\\\\"
outfile.write(printline + "\n")
continue
bestfit = mockbestfits[jj][ii] * scale[ii]
xs = mockxs[jj][ii] * scale[ii]
lowval, highval = sixtyeigthinterval(bestfit, xs, quantile=quantile)
dlow, dhigh = bestfit - lowval, highval - bestfit
# Prepare
maxerr = numpy.amin(numpy.fabs([dlow, dhigh]))
if math.log10(maxerr) >= 0.0:
value = "$%.0f$" % (bestfit)
if numpy.fabs((dlow - dhigh)) < 1.0:
err = "$\pm$%.0f" % ((dlow + dhigh) / 2.0)
else:
err = "$^{+%.0f}_{-%.0f}$" % (dhigh, dlow)
elif math.log10(maxerr) >= -1.0:
value = "$%.1f$" % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.1:
err = "$\pm$%.1f" % ((dlow + dhigh) / 2.0)
else:
err = "$^{+%.1f}_{-%.1f}$" % (dhigh, dlow)
elif math.log10(maxerr) >= -2.0:
value = "$%.2f$" % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.01:
err = "$\pm$%.2f" % ((dlow + dhigh) / 2.0)
else:
err = "$^{+%.2f}_{-%.2f}$" % (dhigh, dlow)
elif math.log10(maxerr) >= -3.0:
value = "$%.3f$" % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.001:
err = "$\pm$%.3f" % ((dlow + dhigh) / 2.0)
else:
err = "$^{+%.3f}_{-%.3f}$" % (dhigh, dlow)
else:
value = "$%.4f$" % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.0001:
err = "$\pm$%.4f" % ((dlow + dhigh) / 2.0)
else:
err = "$^{+%.4f}_{-%.4f}$" % (dhigh, dlow)
printline += value + "&" + err
if not ii == (len(names) - 1):
printline += "\\\\"
if ii == (len(names) - (len(locs) + 2)):
printline += "\\\\"
if ii < (len(names) - (len(locs) + 1)):
outfile.write(printline + "\n")
outfile.write("\\enddata\n")
outfile.write(cmdline + "\n")
outfile.close()
