def plot_rotcurve_samples(options=None,args=None):
"""Plot sample rotation curves"""
options= set_options(options)
params= numpy.random.permutation(load_samples(args[0]))
rotcurves= []
rs= numpy.linspace(3.5,16.,1001)
add= 0
if options.nooutliermean: add-= 1
if not options.dwarf: add-= 1
for ii in range(options.nsamples):
if options.rotcurve.lower() == 'flat':
thisrc= params[ii][0]*rs**0.*_REFV0
elif options.rotcurve.lower() == 'powerlaw':
thisrc= params[ii][0]*(rs/_REFR0/params[ii][1])**params[ii][6+add]*_REFV0
elif options.rotcurve.lower() == 'linear':
thisrc= (params[ii][0]+params[ii][6+add]*(rs/_REFR0/params[ii][1]-1.))*_REFV0
elif options.rotcurve.lower() == 'quadratic':
thisrc= (params[ii][0]+params[ii][6+add]*(rs/_REFR0/params[ii][1]-1.)+params[ii][7+add]*(rs/_REFR0/params[ii][1]-1)**2.)*_REFV0
elif options.rotcurve.lower() == 'cubic':
thisrc= (params[ii][0]+params[ii][6+add]*(rs/_REFR0/params[ii][1]-1)+params[ii][7+add]*(rs/_REFR0/params[ii][1]-1.)**2.+params[ii][8+add]*(rs/_REFR0/params[ii][1]-1.)**3.)*_REFV0
rotcurves.append(thisrc)
bovy_plot.bovy_print(fig_width=8.)
ii= 0
bovy_plot.bovy_plot(rs,rotcurves[ii],'-',color='0.65',
xlabel=r'$R\ [\mathrm{kpc}]$',
ylabel=r'$V_c\ [\mathrm{km\ s}^{-1}]$',
xrange=[0.,20.],
yrange=[150.,300.])
for ii in range(1,options.nsamples):
bovy_plot.bovy_plot(rs,rotcurves[ii],'-',color='0.65',overplot=True,alpha=0.1)
if _PLOTM31:
#Read file
m31data= numpy.loadtxt('../data/m31.dat',comments='#')
rm31= m31data[:,0]
vcm31= m31data[:,1]
bovy_plot.bovy_plot(rm31,vcm31,'ks',overplot=True,mfc='none',mew=2.)
bovy_plot.bovy_text(17.,260.,r'$\mathrm{M31}$',size=14.)
indx= (rm31 > 15.2)*(rm31 <= 16.8)
bovy_plot.bovy_plot([17.,rm31[indx]],[260.,vcm31[indx]],'k-',
overplot=True)
bovy_plot.bovy_end_print(options.plotfilename)
return None
def plot_rotcurve_samples(options=None, args=None):
"""Plot sample rotation curves"""
options = set_options(options)
params = numpy.random.permutation(load_samples(args[0]))
rotcurves = []
rs = numpy.linspace(3.5, 16., 1001)
add = 0
if options.nooutliermean: add -= 1
if not options.dwarf: add -= 1
for ii in range(options.nsamples):
if options.rotcurve.lower() == 'flat':
thisrc = params[ii][0] * rs**0. * _REFV0
elif options.rotcurve.lower() == 'powerlaw':
thisrc = params[ii][0] * (
rs / _REFR0 / params[ii][1])**params[ii][6 + add] * _REFV0
elif options.rotcurve.lower() == 'linear':
thisrc = (params[ii][0] + params[ii][6 + add] *
(rs / _REFR0 / params[ii][1] - 1.)) * _REFV0
elif options.rotcurve.lower() == 'quadratic':
thisrc = (params[ii][0] + params[ii][6 + add] *
(rs / _REFR0 / params[ii][1] - 1.) +
params[ii][7 + add] *
(rs / _REFR0 / params[ii][1] - 1)**2.) * _REFV0
elif options.rotcurve.lower() == 'cubic':
thisrc = (params[ii][0] + params[ii][6 + add] *
(rs / _REFR0 / params[ii][1] - 1) + params[ii][7 + add] *
(rs / _REFR0 / params[ii][1] - 1.)**2. +
params[ii][8 + add] *
(rs / _REFR0 / params[ii][1] - 1.)**3.) * _REFV0
rotcurves.append(thisrc)
bovy_plot.bovy_print(fig_width=8.)
ii = 0
bovy_plot.bovy_plot(rs,
rotcurves[ii],
'-',
color='0.65',
xlabel=r'$R\ [\mathrm{kpc}]$',
ylabel=r'$V_c\ [\mathrm{km\ s}^{-1}]$',
xrange=[0., 20.],
yrange=[150., 300.])
for ii in range(1, options.nsamples):
bovy_plot.bovy_plot(rs,
rotcurves[ii],
'-',
color='0.65',
overplot=True,
alpha=0.1)
if _PLOTM31:
#Read file
m31data = numpy.loadtxt('../data/m31.dat', comments='#')
rm31 = m31data[:, 0]
vcm31 = m31data[:, 1]
bovy_plot.bovy_plot(rm31,
vcm31,
'ks',
overplot=True,
mfc='none',
mew=2.)
bovy_plot.bovy_text(17., 260., r'$\mathrm{M31}$', size=14.)
indx = (rm31 > 15.2) * (rm31 <= 16.8)
bovy_plot.bovy_plot([17., rm31[indx]], [260., vcm31[indx]],
'k-',
overplot=True)
bovy_plot.bovy_end_print(options.plotfilename)
return None
def plot_rovo(filename, plotfilename):
if not os.path.exists(filename):
raise IOError("given filename does not exist")
savefile = open(filename, 'rb')
params = pickle.load(savefile)
savefile.close()
if _ANALYTIC: #Calculate by fixing everything except for Ro anv vo
options = plot_pdfs.set_options(None)
nros = 15
noos = 15
ros = numpy.linspace(7., 13., nros)
oos = numpy.linspace(20., 30., noos)
ll = numpy.zeros((noos, nros))
for ii in range(noos):
if not _MULTI is None:
theseparamss = []
for jj in range(nros):
theseparams = copy.copy(params)
theseparams[0] = oos[ii] * ros[jj] / _REFV0
theseparams[1] = ros[jj] / _REFR0
theseparamss.append(theseparams)
thisll = multi.parallel_map(
(lambda x: numpy.sum(
logl.logl(init=theseparamss[x], options=options))),
range(nros),
numcores=numpy.amin(
[nros, _MULTI,
multiprocessing.cpu_count()]))
ll[ii, :] = thisll
else:
for jj in range(nros):
theseparams = copy.copy(params)
theseparams[0] = oos[ii] * ros[jj] / _REFV0
theseparams[1] = ros[jj] / _REFR0
ll[ii, jj] = numpy.sum(
logl.logl(init=theseparams, options=options))
#Normalize
ll -= logsumexp(ll)
ll = numpy.exp(ll)
levels = list(special.erf(0.5 * numpy.arange(1, 4)))
bovy_plot.bovy_dens2d(
ll.T,
origin='lower',
levels=levels,
xlabel=r'$\Omega_0\ [\mathrm{km\ s}^{-1}\ \mathrm{kpc}^{-1}]$',
ylabel=r'$R_0\ [\mathrm{kpc}]$',
xrange=[20., 35.],
yrange=[7., 13.],
contours=True,
cntrcolors='k',
onedhists=True,
cmap='gist_yarg')
else:
vos = numpy.array([s[0] for s in params]) * _REFV0
ros = numpy.array([s[1] for s in params]) * _REFR0
bovy_plot.bovy_print()
levels = list(special.erf(0.5 * numpy.arange(1, 4)))
levels.append(1.01) #HACK to not plot outliers
bovy_plot.scatterplot(
vos / ros,
ros,
'k,',
levels=levels,
xlabel=r'$\Omega_0\ [\mathrm{km\ s}^{-1}\ \mathrm{kpc}^{-1}]$',
ylabel=r'$R_0\ [\mathrm{kpc}]$',
bins=31,
xrange=[200. / 8., 250. / 8.],
yrange=[7., 9.],
contours=True,
cntrcolors='k',
onedhists=True,
cmap='gist_yarg')
bovy_plot.bovy_end_print(plotfilename)
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 plot_rovo(filename,plotfilename):
if not os.path.exists(filename):
raise IOError("given filename does not exist")
savefile= open(filename,'rb')
params= pickle.load(savefile)
savefile.close()
if _ANALYTIC: #Calculate by fixing everything except for Ro anv vo
options= plot_pdfs.set_options(None)
nros= 15
noos= 15
ros= numpy.linspace(7.,13.,nros)
oos= numpy.linspace(20.,30.,noos)
ll= numpy.zeros((noos,nros))
for ii in range(noos):
if not _MULTI is None:
theseparamss= []
for jj in range(nros):
theseparams= copy.copy(params)
theseparams[0]= oos[ii]*ros[jj]/_REFV0
theseparams[1]= ros[jj]/_REFR0
theseparamss.append(theseparams)
thisll= multi.parallel_map((lambda x: numpy.sum(logl.logl(init=theseparamss[x],options=options))),
range(nros),
numcores=numpy.amin([nros,_MULTI,multiprocessing.cpu_count()]))
ll[ii,:]= thisll
else:
for jj in range(nros):
theseparams= copy.copy(params)
theseparams[0]= oos[ii]*ros[jj]/_REFV0
theseparams[1]= ros[jj]/_REFR0
ll[ii,jj]= numpy.sum(logl.logl(init=theseparams,
options=options))
#Normalize
ll-= logsumexp(ll)
ll= numpy.exp(ll)
levels= list(special.erf(0.5*numpy.arange(1,4)))
bovy_plot.bovy_dens2d(ll.T,origin='lower',levels=levels,
xlabel=r'$\Omega_0\ [\mathrm{km\ s}^{-1}\ \mathrm{kpc}^{-1}]$',
ylabel=r'$R_0\ [\mathrm{kpc}]$',
xrange=[20.,35.],
yrange=[7.,13.],
contours=True,
cntrcolors='k',
onedhists=True,
cmap='gist_yarg')
else:
vos= numpy.array([s[0] for s in params])*_REFV0
ros= numpy.array([s[1] for s in params])*_REFR0
bovy_plot.bovy_print()
levels= list(special.erf(0.5*numpy.arange(1,4)))
levels.append(1.01) #HACK to not plot outliers
bovy_plot.scatterplot(vos/ros,ros,'k,',levels=levels,
xlabel=r'$\Omega_0\ [\mathrm{km\ s}^{-1}\ \mathrm{kpc}^{-1}]$',
ylabel=r'$R_0\ [\mathrm{kpc}]$',
bins=31,
xrange=[200./8.,250./8.],
yrange=[7.,9.],
contours=True,
cntrcolors='k',
onedhists=True,
cmap='gist_yarg')
bovy_plot.bovy_end_print(plotfilename)
def plot_rotcurve_samples(options=None,args=None):
"""Plot sample rotation curves"""
options= set_options(options)
params= numpy.random.permutation(load_samples(args[0]))
if options.removerooutliers:
indx= numpy.zeros(len(params),dtype='bool')
indx[:]= True
for ii in range(len(params)):
if params[ii][1] > (9./_REFR0): indx[ii]= False
params= params[indx]
options.nsamples= numpy.amin([options.nsamples,len(params)])
print len(params)
bestfitparams= load_samples(args[1])
rotcurves= []
rs= numpy.linspace(3.5,14.,1001)
fs= numpy.zeros((len(rs),options.nsamples))
besfitrc= numpy.zeros_like(rs)
add= 0
if options.nooutliermean: add-= 1
if not options.dwarf: add-= 1
if options.rotcurve.lower() == 'flat':
bestfitrc= bestfitparams[0]*rs**0.*_REFV0
elif options.rotcurve.lower() == 'powerlaw':
bestfitrc= bestfitparams[0]*(rs/_REFR0/bestfitparams[1])**bestfitparams[6+add]*_REFV0
elif options.rotcurve.lower() == 'linear':
bestfitrc= (bestfitparams[0]+bestfitparams[6+add]*(rs/_REFR0/bestfitparams[1]-1.))*_REFV0
elif options.rotcurve.lower() == 'quadratic':
bestfitrc= (bestfitparams[0]+bestfitparams[6+add]*(rs/_REFR0/bestfitparams[1]-1.)+bestfitparams[7+add]*(rs/_REFR0/bestfitparams[1]-1)**2.)*_REFV0
elif options.rotcurve.lower() == 'cubic':
bestfitrc= (bestfitparams[0]+bestfitparams[6+add]*(rs/_REFR0/bestfitparams[1]-1)+bestfitparams[7+add]*(rs/_REFR0/bestfitparams[1]-1.)**2.+bestfitparams[8+add]*(rs/_REFR0/bestfitparams[1]-1.)**3.)*_REFV0
for ii in range(options.nsamples):
if options.rotcurve.lower() == 'flat':
thisrc= params[ii][0]*rs**0.*_REFV0
elif options.rotcurve.lower() == 'powerlaw':
thisrc= params[ii][0]*(rs/_REFR0/params[ii][1])**params[ii][6+add]*_REFV0
elif options.rotcurve.lower() == 'linear':
thisrc= (params[ii][0]+params[ii][6+add]*(rs/_REFR0/params[ii][1]-1.))*_REFV0
elif options.rotcurve.lower() == 'quadratic':
thisrc= (params[ii][0]+params[ii][6+add]*(rs/_REFR0/params[ii][1]-1.)+params[ii][7+add]*(rs/_REFR0/params[ii][1]-1)**2.)*_REFV0
elif options.rotcurve.lower() == 'cubic':
thisrc= (params[ii][0]+params[ii][6+add]*(rs/_REFR0/params[ii][1]-1)+params[ii][7+add]*(rs/_REFR0/params[ii][1]-1.)**2.+params[ii][8+add]*(rs/_REFR0/params[ii][1]-1.)**3.)*_REFV0
fs[:,ii]= thisrc
#Now plot the mean and std-dev from the posterior
rcmean= numpy.zeros(len(rs))
nsigs= 3
rcsigs= numpy.zeros((len(rs),2*nsigs))
#Record mean and std-devs
rcmean[:]= numpy.mean(fs,axis=1)
bovy_plot.bovy_print(fig_width=8.)
bovy_plot.bovy_plot(rs,bestfitrc,'k-',
xlabel=r'$R\ [\mathrm{kpc}]$',
ylabel=r'$V_c\ [\mathrm{km\ s}^{-1}]$',
xrange=[0.,20.],
yrange=[150.,300.])
for ii in range(nsigs):
for jj in range(len(rs)):
thisf= sorted(fs[jj,:])
thiscut= 0.5*special.erfc((ii+1.)/math.sqrt(2.))
rcsigs[jj,2*ii]= thisf[int(math.floor(thiscut*options.nsamples))]
thiscut= 1.-thiscut
rcsigs[jj,2*ii+1]= thisf[int(math.floor(thiscut*options.nsamples))]
colord, cc= (1.-0.75)/(nsigs+1.), 1
nsigma= nsigs
pyplot.fill_between(rs,rcsigs[:,0],rcsigs[:,1],color='0.75')
while nsigma > 1:
pyplot.fill_between(rs,rcsigs[:,cc+1],rcsigs[:,cc-1],
color='%f' % (.75+colord*cc))
pyplot.fill_between(rs,rcsigs[:,cc],rcsigs[:,cc+2],
color='%f' % (.75+colord*cc))
cc+= 2
nsigma-= 1
bovy_plot.bovy_plot(rs,bestfitrc,'k-',overplot=True)
if options.plotm31:
#Read file
m31data= numpy.loadtxt('../data/m31.dat',comments='#')
rm31= m31data[:,0]
vcm31= m31data[:,1]
bovy_plot.bovy_plot(rm31,vcm31,'ks',overplot=True,mfc='none',mew=2.)
bovy_plot.bovy_text(17.,260.,r'$\mathrm{M31}$',size=14.)
indx= (rm31 > 15.2)*(rm31 <= 16.8)
bovy_plot.bovy_plot([17.,rm31[indx]],[260.,vcm31[indx]],'k-',
overplot=True)
#Labels
if options.rotcurve.lower() == 'flat':
print "This is silly"
elif options.rotcurve.lower() == 'powerlaw':
bovy_plot.bovy_text(r'$\mathrm{Power\!\!-\!\!law\ rotation\ curve\ model}$',
top_left=True,size=16.)
elif options.rotcurve.lower() == 'linear':
bovy_plot.bovy_text(r'$\mathrm{Linearly rising\ rotation\ curve\ model}$',
top_left=True,size=16.)
elif options.rotcurve.lower() == 'quadratic':
bovy_plot.bovy_text(r'$\mathrm{Quadratic polynomial\ rotation\ curve\ model}$',
top_left=True,size=16.)
elif options.rotcurve.lower() == 'cubic':
bovy_plot.bovy_text(r'$\mathrm{Cubic\ polynomial\ rotation\ curve\ model}$',
top_left=True,size=16.)
if options.removerooutliers:
bovy_plot.bovy_text(r'$R_0 < 9\,\mathrm{kpc}$',
top_right=True,size=16.)
if options.addmock2:
bovy_plot.bovy_text(r'$\mathrm{Mock\ 2}$',
bottom_right=True,size=16.)
bovy_plot.bovy_end_print(options.plotfilename)
return None
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()
