def checkAbundanceScatterClusters():
# First read the cluster data
cldata= read_clusterdata.read_caldata()
# Read the allStar data to match
# For each of the calibration open clusters, calculate the offset from the
# mean in our FEHTAG and AFETAG
clusters= ['M71','N2158','N2420','N188','M67','N7789','N6819',
'N6791']
fehoffset= []
afeoffset= []
for cluster in clusters:
tdata= cldata[cldata['CLUSTER'] == cluster.upper()]
tdata= tdata[(tdata['TEFF'] < _TEFFMAX)\
*(tdata['TEFF'] > _TEFFMIN)\
*(tdata['LOGG'] < 3.5)]
# Compute the average feh and afe and save the offsets
medianfeh= numpy.median(tdata['FE_H'])
medianafe= numpy.median(tdata[define_rcsample._AFETAG])
fehoffset.extend(tdata['FE_H']-medianfeh)
afeoffset.extend(tdata[define_rcsample._AFETAG]-medianafe)
if cluster == 'M67': print medianfeh, medianafe, len(tdata)
fehoffset= numpy.array(fehoffset)
afeoffset= numpy.array(afeoffset)
print 'FE_H scatter %g' % (numpy.nanstd(fehoffset[numpy.fabs(fehoffset) < 0.3]))
print 'A_FE scatter %g' % (numpy.nanstd(afeoffset[numpy.fabs(afeoffset) < 0.3]))
gindx= (numpy.fabs(fehoffset) < 0.3)*(numpy.fabs(afeoffset) < 0.3)
print 'FE_H/A_FE correlation %g' % (numpy.mean(afeoffset[gindx]*fehoffset[gindx])/numpy.nanstd(fehoffset[numpy.fabs(fehoffset) < 0.3])/numpy.nanstd(afeoffset[numpy.fabs(afeoffset) < 0.3]))
print 'FE_H robust scatter %g' % (1.4826*numpy.median(numpy.fabs(fehoffset)))
print 'A_FE robust scatter %g' % (1.4826*numpy.median(numpy.fabs(afeoffset)))
bovy_plot.bovy_print()
bovy_plot.bovy_hist(fehoffset,range=[-0.3,0.3],bins=31,histtype='step')
bovy_plot.bovy_hist(afeoffset,range=[-0.3,0.3],bins=31,histtype='step',
overplot=True)
bovy_plot.bovy_end_print('test.png')
return None
def plot_pdfs_l(plotfilename):
lp= potential.LogarithmicHaloPotential(q=0.9,normalize=1.)
aAI= actionAngleIsochroneApprox(b=0.8,pot=lp)
obs= numpy.array([1.56148083,0.35081535,-1.15481504,
0.88719443,-0.47713334,0.12019596])
sdf= streamdf(_SIGV/220.,progenitor=Orbit(obs),pot=lp,aA=aAI,
leading=True,nTrackChunks=_NTRACKCHUNKS,
vsun=[0.,30.24*8.,0.],
tdisrupt=4.5/bovy_conversion.time_in_Gyr(220.,8.),
multi=_NTRACKCHUNKS)
sdft= streamdf(_SIGV/220.,progenitor=Orbit(obs),pot=lp,aA=aAI,
leading=False,nTrackChunks=_NTRACKCHUNKS,
vsun=[0.,30.24*8.,0.],
tdisrupt=4.5/bovy_conversion.time_in_Gyr(220.,8.),
multi=_NTRACKCHUNKS)
#Calculate the density as a function of l, p(l)
#Sample from sdf
llbd= sdf.sample(n=40000,lb=True)
tlbd= sdft.sample(n=50000,lb=True)
b,e= numpy.histogram(llbd[0],bins=101,normed=True)
t= ((numpy.roll(e,1)-e)/2.+e)[1:]
lspl= interpolate.UnivariateSpline(t,numpy.log(b),k=3,s=1.)
lls= numpy.linspace(t[0],t[-1],_NLS)
lps= numpy.exp(lspl(lls))
lps/= numpy.sum(lps)*(lls[1]-lls[0])*2.
b,e= numpy.histogram(tlbd[0],bins=101,normed=True)
t= ((numpy.roll(e,1)-e)/2.+e)[1:]
tspl= interpolate.UnivariateSpline(t,numpy.log(b),k=3,s=0.5)
tls= numpy.linspace(t[0],t[-1],_NLS)
tps= numpy.exp(tspl(tls))
tps/= numpy.sum(tps)*(tls[1]-tls[0])*2.
bovy_plot.bovy_print(fig_width=8.25,fig_height=3.5)
bovy_plot.bovy_plot(lls,lps,'k-',lw=1.5,
xlabel=r'$\mathrm{Galactic\ longitude}\,(\mathrm{deg})$',
ylabel=r'$p(l)$',
xrange=[65.,250.],
yrange=[0.,1.2*numpy.nanmax(numpy.hstack((lps,tps)))])
bovy_plot.bovy_plot(tls,tps,'k-',lw=1.5,overplot=True)
#Also plot the stream histogram
#Read stream
data= numpy.loadtxt(os.path.join(_STREAMSNAPDIR,'gd1_evol_hitres_01312.dat'),
delimiter=',')
#Transform to (l,b)
XYZ= bovy_coords.galcenrect_to_XYZ(data[:,1],data[:,3],data[:,2],Xsun=8.)
lbd= bovy_coords.XYZ_to_lbd(XYZ[0],XYZ[1],XYZ[2],degree=True)
aadata= numpy.loadtxt(os.path.join(_STREAMSNAPAADIR,
'gd1_evol_hitres_aa_01312.dat'),
delimiter=',')
thetar= aadata[:,6]
thetar= (numpy.pi+(thetar-numpy.median(thetar))) % (2.*numpy.pi)
indx= numpy.fabs(thetar-numpy.pi) > (5.*numpy.median(numpy.fabs(thetar-numpy.median(thetar))))
lbd= lbd[indx,:]
bovy_plot.bovy_hist(lbd[:,0],bins=40,range=[65.,250.],
histtype='step',normed=True,
overplot=True,
lw=1.5,color='k')
bovy_plot.bovy_end_print(plotfilename)
def plotSkew(parser):
(options,args)= parser.parse_args()
if len(args) == 0:
parser.print_help()
return
savefilename= args[0]
if os.path.exists(savefilename):
savefile= open(savefilename,'rb')
skews= pickle.load(savefile)
gaussskews= pickle.load(savefile)
type= pickle.load(savefile)
band= pickle.load(savefile)
mean= pickle.load(savefile)
taus= pickle.load(savefile)
savefile.close()
else:
parser.print_help()
return
#Accumulate
keys= skews.keys()
allskews= numpy.zeros((len(keys),len(taus)))
allgaussskews= numpy.zeros((len(keys),gaussskews[keys[0]].shape[0],
len(taus)))
for ii, key in enumerate(keys):
allskews[ii,:]= -skews[key] #go to regular definition
allgaussskews[ii,:]= -gaussskews[key]
#Statistic
q= 0.99
statistic= numpy.median
if not options.indx is None:
print "indx option not allowed"
return None
indx= numpy.all(numpy.isnan(allskews),axis=1)
allskews= allskews[True-indx,:]
allgaussskews= allgaussskews[True-indx,:,:]
#Median
medianskew= statistic(allskews,axis=0)
mediangaussskew= statistic(allgaussskews,axis=0)
#Determine 1-sigma
sigma= quantile(mediangaussskew,q=q)
#Plot
tauindx= 5
print "Showing tau %f" % (taus[tauindx]*365.25)
bovy_plot.bovy_print(fig_width=7.)
bovy_plot.bovy_hist(allskews[:,tauindx],bins=31,
color='k',normed=True,histtype='step',
xlabel=r'$\mathrm{skew}(\tau = %i\ \mathrm{days})$' % (int(taus[tauindx]*365.25)))
bovy_plot.bovy_plot([numpy.median(allskews[:,tauindx]),numpy.median(allskews[:,tauindx])],
[0.,10.],'k-',overplot=True)
bovy_plot.bovy_plot([numpy.mean(allskews[:,tauindx]),numpy.mean(allskews[:,tauindx])],
[0.,10.],'k--',overplot=True)
bovy_plot.bovy_end_print(options.plotfilename)
return None
def plot_seguem10(plotfilename):
savefile= open('../potential-fits/fitall_10ksamples_derived_whalomass.sav','rb')
data= pickle.load(savefile)
savefile.close()
mh= numpy.array([x[8] for x in data])
bovy_plot.bovy_print()
bovy_plot.bovy_hist(mh,range=[0.,10.],
bins=71,histtype='step',color='k',
xlabel=r'$M_{\mathrm{halo}}(<10\,\mathrm{kpc})\,(10^{10}\,M_\odot)$',
normed=True)
xs= numpy.linspace(0.,10.,1001)
bovy_plot.bovy_plot(xs,1./numpy.sqrt(2.*numpy.pi*numpy.var(mh[True-numpy.isnan(mh)]))*numpy.exp(-0.5*(xs-numpy.mean(mh[True-numpy.isnan(mh)]))**2./numpy.var(mh[True-numpy.isnan(mh)])),
'k-',lw=2,overplot=True)
bovy_plot.bovy_end_print(plotfilename)
def checkAbundanceScatterClusters():
# First read the cluster data
cldata = read_clusterdata.read_caldata()
# Read the allStar data to match
# For each of the calibration open clusters, calculate the offset from the
# mean in our FEHTAG and AFETAG
clusters = [
'M71', 'N2158', 'N2420', 'N188', 'M67', 'N7789', 'N6819', 'N6791'
]
fehoffset = []
afeoffset = []
for cluster in clusters:
tdata = cldata[cldata['CLUSTER'] == cluster.upper()]
tdata= tdata[(tdata['TEFF'] < _TEFFMAX)\
*(tdata['TEFF'] > _TEFFMIN)\
*(tdata['LOGG'] < 3.5)]
# Compute the average feh and afe and save the offsets
medianfeh = numpy.median(tdata['FE_H'])
medianafe = numpy.median(tdata[define_rcsample._AFETAG])
fehoffset.extend(tdata['FE_H'] - medianfeh)
afeoffset.extend(tdata[define_rcsample._AFETAG] - medianafe)
if cluster == 'M67': print medianfeh, medianafe, len(tdata)
fehoffset = numpy.array(fehoffset)
afeoffset = numpy.array(afeoffset)
print 'FE_H scatter %g' % (numpy.nanstd(
fehoffset[numpy.fabs(fehoffset) < 0.3]))
print 'A_FE scatter %g' % (numpy.nanstd(
afeoffset[numpy.fabs(afeoffset) < 0.3]))
gindx = (numpy.fabs(fehoffset) < 0.3) * (numpy.fabs(afeoffset) < 0.3)
print 'FE_H/A_FE correlation %g' % (
numpy.mean(afeoffset[gindx] * fehoffset[gindx]) /
numpy.nanstd(fehoffset[numpy.fabs(fehoffset) < 0.3]) /
numpy.nanstd(afeoffset[numpy.fabs(afeoffset) < 0.3]))
print 'FE_H robust scatter %g' % (1.4826 *
numpy.median(numpy.fabs(fehoffset)))
print 'A_FE robust scatter %g' % (1.4826 *
numpy.median(numpy.fabs(afeoffset)))
bovy_plot.bovy_print()
bovy_plot.bovy_hist(fehoffset, range=[-0.3, 0.3], bins=31, histtype='step')
bovy_plot.bovy_hist(afeoffset,
range=[-0.3, 0.3],
bins=31,
histtype='step',
overplot=True)
bovy_plot.bovy_end_print('test.png')
return None
def plot_pdfs_x(plotfilename):
lp= potential.LogarithmicHaloPotential(q=0.9,normalize=1.)
aAI= actionAngleIsochroneApprox(b=0.8,pot=lp)
obs= numpy.array([1.56148083,0.35081535,-1.15481504,
0.88719443,-0.47713334,0.12019596])
sdft= streamdf(_SIGV/220.,progenitor=Orbit(obs),pot=lp,aA=aAI,
leading=False,nTrackChunks=_NTRACKCHUNKS,
vsun=[0.,30.24*8.,0.],
tdisrupt=4.5/bovy_conversion.time_in_Gyr(220.,8.),
multi=_NTRACKCHUNKS)
#Calculate the density as a function of l, p(l)
txs= numpy.linspace(3.,12.4,_NLS)
tlogps= multi.parallel_map((lambda x: sdft.callMarg([txs[x]/8.,None,None,None,None,None],
interp=True,ngl=_NGL,
nsigma=3)),
range(_NLS),
numcores=numpy.amin([_NLS,
multiprocessing.cpu_count()]))
tlogps= numpy.array(tlogps)
tlogps[numpy.isnan(tlogps)]= -100000000000000000.
tps= numpy.exp(tlogps-logsumexp(tlogps))
tps/= numpy.nansum(tps)*(txs[1]-txs[0])
bovy_plot.bovy_print()
bovy_plot.bovy_plot(txs,tps,'k-',lw=1.5,
xlabel=r'$X\,(\mathrm{kpc})$',
ylabel=r'$p(X)$',
xrange=[3.,12.4],
yrange=[0.,1.2*numpy.nanmax(tps)])
bovy_plot.bovy_plot(txs,tps,'k-',lw=1.5,overplot=True)
#Also plot the stream histogram
#Read stream
data= numpy.loadtxt(os.path.join(_STREAMSNAPDIR,'gd1_evol_hitres_01312.dat'),
delimiter=',')
aadata= numpy.loadtxt(os.path.join(_STREAMSNAPAADIR,
'gd1_evol_hitres_aa_01312.dat'),
delimiter=',')
thetar= aadata[:,6]
thetar= (numpy.pi+(thetar-numpy.median(thetar))) % (2.*numpy.pi)
indx= thetar-numpy.pi < -(5.*numpy.median(numpy.fabs(thetar-numpy.median(thetar))))
data= data[indx,:]
bovy_plot.bovy_hist(data[:,1],bins=20,range=[3.,12.4],
histtype='step',normed=True,
overplot=True,
lw=1.5,color='k')
bovy_plot.bovy_end_print(plotfilename)
def plotloglhist(options,args):
#Go through all of the bins
if options.sample.lower() == 'g':
npops= 62
elif options.sample.lower() == 'k':
npops= 30
for ii in range(npops):
if _NOTDONEYET:
spl= options.restart.split('.')
else:
spl= args[0].split('.')
newname= ''
for jj in range(len(spl)-1):
newname+= spl[jj]
if not jj == len(spl)-2: newname+= '.'
newname+= '_%i.' % ii
newname+= spl[-1]
savefile= open(newname,'rb')
try:
if not _NOTDONEYET:
params= pickle.load(savefile)
mlogl= pickle.load(savefile)
logl= pickle.load(savefile)
except:
continue
finally:
savefile.close()
if _NOTDONEYET:
logl[(logl == 0.)]= -numpy.finfo(numpy.dtype(numpy.float64)).max
bovy_plot.bovy_print()
bovy_plot.bovy_hist(logl.flatten()[(logl.flatten() >-1000000.)],
xrange=[numpy.nanmax(logl)-50.,numpy.nanmax(logl)],
xlabel=r'$\log \mathcal{L}$',
histtype='step',color='k')
#Plotname
spl= options.outfilename.split('.')
newname= ''
for jj in range(len(spl)-1):
newname+= spl[jj]
if not jj == len(spl)-2: newname+= '.'
newname+= '_%i.' % ii
newname+= spl[-1]
bovy_plot.bovy_end_print(newname)
return None
def simplePlot(location=4242,
plotfile=None,
predict=False,
nv=11,
dmax=10. / 8.,
**kwargs):
"""
NAME:
simplePlot
PURPOSE:
make a simple histogram for a given location
INPUT:
location - location ID
+readVclosData inputs
OPTIONAL INPUT:
plotfile= if set, save plot to this file
OUTPUT:
plot to display or file
HISTORY:
2012-01-25 - Written - Bovy (IAS)
"""
#Read data
data = readVclosData(**kwargs)
data = data[(data['LOCATION'] == location)]
if not plotfile is None:
bovy_plot.bovy_print()
range = [-200., 200.]
hist, xvec, p = bovy_plot.bovy_hist(
data['VHELIO'],
range=range,
bins=31,
histtype='step',
color='k',
xlabel=
r'$\mathrm{heliocentric}\ v_{\mathrm{los}}\ [\mathrm{km\ s}^{-1}]$')
#Prediction
if predict:
pred_vs = numpy.linspace(range[0], range[1], nv)
pred_dist = _calc_pred(pred_vs, location, numpy.mean(data['GLON']),
dmax)
data_int = numpy.sum(hist) * (xvec[1] - xvec[0])
pred_dist *= data_int / numpy.sum(pred_dist) / (pred_vs[1] -
pred_vs[0])
bovy_plot.bovy_plot(pred_vs,
pred_dist,
'-',
color='0.65',
overplot=True)
#Add text
bovy_plot.bovy_text(r'$\mathrm{location}\ =\ %i$' % location + '\n' +
r'$l\ \approx\ %.0f^\circ$' % numpy.mean(data['GLON']),
top_right=True,
size=14.)
if not plotfile is None:
bovy_plot.bovy_end_print(plotfile)
def plot_data_feh(location=0,
plotfilename=os.path.join(OUTDIR,'data_h_jk.'+OUTEXT)):
data= readVclosData()
#Good feh
data= data[(data['FEH']!= -9999.00)]
if not location is None:
if location == 0:
locs= set(data['LOCATION'])
for l in locs:
plot_data_feh(location=l,
plotfilename=os.path.join(OUTDIR,'data_feh_%i.' % l +OUTEXT))
return None
data= data[(data['LOCATION'] == location)]
meanfeh= numpy.mean(data['FEH'])
sigfeh= numpy.std(data['FEH'])
bovy_plot.bovy_print()
bovy_plot.bovy_hist(data['FEH'],
xlabel=r'$[\mathrm{Fe/H}]$',
xrange=[meanfeh-1.,meanfeh+1],
bins=16)
bovy_plot.bovy_text(r'$\sigma = %.2f$' % sigfeh,top_right=True)
bovy_plot.bovy_end_print(plotfilename)
return None
def plot_data_feh(location=0,
plotfilename=os.path.join(OUTDIR, 'data_h_jk.' + OUTEXT)):
data = readVclosData()
#Good feh
data = data[(data['FEH'] != -9999.00)]
if not location is None:
if location == 0:
locs = set(data['LOCATION'])
for l in locs:
plot_data_feh(location=l,
plotfilename=os.path.join(
OUTDIR, 'data_feh_%i.' % l + OUTEXT))
return None
data = data[(data['LOCATION'] == location)]
meanfeh = numpy.mean(data['FEH'])
sigfeh = numpy.std(data['FEH'])
bovy_plot.bovy_print()
bovy_plot.bovy_hist(data['FEH'],
xlabel=r'$[\mathrm{Fe/H}]$',
xrange=[meanfeh - 1., meanfeh + 1],
bins=16)
bovy_plot.bovy_text(r'$\sigma = %.2f$' % sigfeh, top_right=True)
bovy_plot.bovy_end_print(plotfilename)
return None
def simplePlot(location=4242,plotfile=None,predict=False,nv=11,dmax=10./8.,
**kwargs):
"""
NAME:
simplePlot
PURPOSE:
make a simple histogram for a given location
INPUT:
location - location ID
+readVclosData inputs
OPTIONAL INPUT:
plotfile= if set, save plot to this file
OUTPUT:
plot to display or file
HISTORY:
2012-01-25 - Written - Bovy (IAS)
"""
#Read data
data= readVclosData(**kwargs)
data= data[(data['LOCATION'] == location)]
if not plotfile is None:
bovy_plot.bovy_print()
range= [-200.,200.]
hist, xvec, p= bovy_plot.bovy_hist(data['VHELIO'],range=range,bins=31,
histtype='step',color='k',
xlabel=r'$\mathrm{heliocentric}\ v_{\mathrm{los}}\ [\mathrm{km\ s}^{-1}]$')
#Prediction
if predict:
pred_vs= numpy.linspace(range[0],range[1],nv)
pred_dist= _calc_pred(pred_vs,location,numpy.mean(data['GLON']),dmax)
data_int= numpy.sum(hist)*(xvec[1]-xvec[0])
pred_dist*= data_int/numpy.sum(pred_dist)/(pred_vs[1]-pred_vs[0])
bovy_plot.bovy_plot(pred_vs,pred_dist,'-',color='0.65',overplot=True)
#Add text
bovy_plot.bovy_text(r'$\mathrm{location}\ =\ %i$' % location
+'\n'
+r'$l\ \approx\ %.0f^\circ$' % numpy.mean(data['GLON']),
top_right=True,size=14.)
if not plotfile is None:
bovy_plot.bovy_end_print(plotfile)
logpiso,
logpisodwarf,iso)),
range(options.nvlos),
numcores=numpy.amin([len(vlos),multiprocessing.cpu_count(),options.multi]))
else:
for ii in range(options.nvlos):
print ii
pvlosalljhk[ii]= pvlosplate(params,vlos[ii],alljhkdata,df,options,
logpiso,logpisodwarf,iso)
pvlosalljhk-= logsumexp(pvlosalljhk)
pvlosalljhk= numpy.exp(pvlosalljhk)
#Plot data
bovy_plot.bovy_print()
hist, xvec, p= bovy_plot.bovy_hist(thesedata['VHELIO'],
range=[-200.,200.],
bins=31,
histtype='step',color='k',
xlabel=r'$\mathrm{heliocentric}\ V_{\mathrm{los}}\ [\mathrm{km\ s}^{-1}]$')
#Normalize prediction
data_int= numpy.sum(hist)*(xvec[1]-xvec[0])
pvlos*= data_int/numpy.sum(pvlos)/(vlos[1]-vlos[0])
bovy_plot.bovy_plot(vlos,pvlos,'-',color='0.6',overplot=True,lw=3.)
if _PLOTZERO:
pvloszero*= data_int/numpy.sum(pvloszero)/(vlos[1]-vlos[0])
bovy_plot.bovy_plot(vlos,pvloszero,'--',color='0.65',overplot=True,lw=2.)
if _PLOTALLJHK:
pvlosalljhk*= data_int/numpy.sum(pvlosalljhk)/(vlos[1]-vlos[0])
bovy_plot.bovy_plot(vlos,pvlosalljhk,'--',color='0.65',overplot=True,lw=2.)
#Add text
bovy_plot.bovy_text(#r'$\mathrm{location}\ =\ %i$' % location
#+'\n'
def plot_fehafe(basefilename):
#First read the sample
data= apread.rcsample()
data= data[data['SNR'] >= 70.]
if _ADDLLOGGCUT:
data= data[data['ADDL_LOGG_CUT'] == 1]
#R and z ranges to plot
Rmins= [5.,7.,9.]
Rmaxs= [7.,9.,11.]
zmins= [0.,0.5,1.]
zmaxs= [0.5,1.,2.]
nRbins= len(Rmins)
nzbins= len(zmins)
for ii in range(nRbins):
for jj in range(nzbins):
#Cut data to relevant range
tindx= (data['RC_GALR'] > Rmins[ii])\
*(data['RC_GALR'] <= Rmaxs[ii])\
*(numpy.fabs(data['RC_GALZ']) > zmins[jj])\
*(numpy.fabs(data['RC_GALZ']) <= zmaxs[jj])
tdata= data[tindx]
bovy_plot.bovy_print()
if (_SCATTERPLOT or len(tdata) > 1500) and not _ADDBOVYRIX:
bovy_plot.scatterplot(tdata['METALS'],
tdata['ALPHAFE'],
'k.',
levels=[0.68,.90],
#special.erf(numpy.arange(1,3)/numpy.sqrt(2.)),
xrange=[-1.2,0.7],
yrange=[-0.12,0.42],
xlabel=r'$[\mathrm{Fe/H}]$',
ylabel=r'$[\alpha/\mathrm{H}]$',
onedhists=True,bins=31,onedhistsbins=21)
else:
axScatter,axHistx, axHisty=\
bovy_plot.bovy_plot(tdata['METALS'],
tdata['ALPHAFE'],
'k.',
xrange=[-1.2,0.7],
yrange=[-0.12,0.42],
xlabel=r'$[\mathrm{Fe/H}]$',
ylabel=r'$[\alpha/\mathrm{H}]$',
onedhists=True,bins=21)
if _ADDBOVYRIX:
add_bovy_rix(axScatter,Rmins[ii],Rmaxs[ii],zmins[jj],zmaxs[jj])
if not _NOLINE:
#Overplot ridge line
bovy_plot.bovy_plot([-0.8,0.3],[ridge1(-0.8),ridge1(0.3)],
'-',lw=2.,color='y',overplot=True)
bovy_plot.bovy_text(r'$%i < R / \mathrm{kpc} \leq %i$' % (Rmins[ii],Rmaxs[ii]) +'\n'+r'$%.1f < |z| / \mathrm{kpc} \leq %.1f$' % (zmins[jj],zmaxs[jj]),
top_left=True,size=16.)
bovy_plot.bovy_end_print(basefilename+'_%iR%i_%.1fz%.1f.' % (Rmins[ii],Rmaxs[ii],zmins[jj],zmaxs[jj])+_EXT)
#Plot z bins only
zmins= [0.,0.5,1.,2.]
zmaxs= [0.5,1.,2.,4.]
nzbins= len(zmins)
for jj in range(nzbins):
#Cut data to relevant range
tindx= (data['RC_GALR'] > 4.)\
*(data['RC_GALR'] <= 9.)\
*(numpy.fabs(data['RC_GALZ']) > zmins[jj])\
*(numpy.fabs(data['RC_GALZ']) <= zmaxs[jj])
tdata= data[tindx]
bovy_plot.bovy_print()
if (_SCATTERPLOT or len(tdata) > 1500) and not _ADDBOVYRIX:
bovy_plot.scatterplot(tdata['METALS'],
tdata['ALPHAFE'],
'k.',
levels=[0.68,.90],
xrange=[-1.2,0.7],
yrange=[-0.12,0.42],
xlabel=r'$[\mathrm{Fe/H}]$',
ylabel=r'$[\alpha/\mathrm{H}]$',
onedhists=True,bins=31,onedhistsbins=31)
else:
axScatter,axHistx, axHisty=\
bovy_plot.bovy_plot(tdata['METALS'],
tdata['ALPHAFE'],
'k.',
xrange=[-1.2,0.7],
yrange=[-0.12,0.42],
xlabel=r'$[\mathrm{Fe/H}]$',
ylabel=r'$[\alpha/\mathrm{H}]$',
onedhists=True,bins=21)
if _ADDBOVYRIX:
add_bovy_rix(axScatter,4.,9.,zmins[jj],zmaxs[jj])
#Overplot ridge line
bovy_plot.bovy_plot([-0.8,0.3],[ridge1(-0.8),ridge1(0.3)],
'-',lw=2.,color='y',overplot=True)
bovy_plot.bovy_text(r'$%.1f < |z| / \mathrm{kpc} \leq %.1f$' % (zmins[jj],zmaxs[jj]),
top_left=True,size=16.)
bovy_plot.bovy_end_print(basefilename+'_%.1fz%.1f.' % (zmins[jj],zmaxs[jj])+_EXT)
#Plot scatter in the high-alpha sequence
#Histograms in 3 R bins
bovy_plot.bovy_print(fig_width=7.)
overplot= False
colors= ['y','k','r']
labelposx= 0.235
labelposy= [13.,11.,9.]
for ii in range(nRbins):
tindx= (data['RC_GALR'] > Rmins[ii])\
*(data['RC_GALR'] <= Rmaxs[ii])\
*(numpy.fabs(data['RC_GALZ']) <= 3.)
tdata= data[tindx]
hindx= determine_highalpha(tdata)
tdata= tdata[hindx]
bovy_plot.bovy_hist(tdata['ALPHAFE']-ridge1(tdata['METALS']),
color=colors[ii],histtype='step',
bins=28,range=[-0.275,0.25],yrange=[0.,15.],
weights=numpy.ones(len(tdata)),
xlabel=r'$\delta[\alpha\mathrm{/Fe}]$',
overplot=overplot,normed=True)
dafe= tdata['ALPHAFE']-ridge1(tdata['METALS'])
dafe= dafe[(numpy.fabs(tdata['RC_GALZ']) > .5)]
txa, txm, txc= run_xd(dafe)
print txm.flatten(), numpy.sqrt(txc.flatten()), txa[0]*len(dafe)
bovy_plot.bovy_plot([txm[0,0],txm[0,0]],[0.,200.],'--',lw=2.,
color=colors[ii],overplot=True)
bovy_plot.bovy_text(labelposx,labelposy[ii],
r'$%i < R \leq %i: \sigma = %.3f$' % (Rmins[ii],Rmaxs[ii],numpy.sqrt(txc[0,0,0])),
size=16.,color=colors[ii],ha='right')
overplot= True
bovy_plot.bovy_end_print(basefilename+'_afefehhist.%s' % _EXT)
#High SN
bovy_plot.bovy_print(fig_width=7.)
overplot= False
for ii in range(nRbins):
tindx= (data['RC_GALR'] > Rmins[ii])\
*(data['RC_GALR'] <= Rmaxs[ii])\
*(numpy.fabs(data['RC_GALZ']) <= 3.)\
*(data['SNR'] >= 150.)
tdata= data[tindx]
hindx= determine_highalpha(tdata)
tdata= tdata[hindx]
bovy_plot.bovy_hist(tdata['ALPHAFE']-ridge1(tdata['METALS']),
color=colors[ii],histtype='step',
bins=19,range=[-0.275,0.15],yrange=[0.,19.5],
weights=numpy.ones(len(tdata)),
xlabel=r'$\delta[\alpha\mathrm{/Fe}]$',
normed=True,
overplot=overplot)
dafe= tdata['ALPHAFE']-ridge1(tdata['METALS'])
dafe= dafe[(numpy.fabs(tdata['RC_GALZ']) > .5)]
txa, txm, txc= run_xd(dafe)
print txm.flatten(), numpy.sqrt(txc.flatten())
bovy_plot.bovy_plot([txm[0,0],txm[0,0]],[0.,200.],'--',lw=2.,
color=colors[ii],overplot=True)
overplot= True
bovy_plot.bovy_end_print(basefilename+'_afefehhist_highsn.%s' % _EXT)
return None
def plot_2dkinematics(basesavefilename,datafilename=None):
#Plot 2D los field
#First read the sample
if not datafilename is None:
data= fitsio.read(datafilename)
else:
data= apread.rcsample()
if _ADDLLOGGCUT:
data= data[data['ADDL_LOGG_CUT'] == 1]
#Cut
indx= (numpy.fabs(data['RC_GALZ']) < 0.25)*(data['METALS'] > -1000.)
print "Using %i stars for low-Z 2D kinematics analysis" % numpy.sum(indx)
data= data[indx]
pix= pixelize_sample.pixelXY(data)
vmin, vmax= -75., 75.
bovy_plot.bovy_print()
pix.plot('VHELIO_AVG',
zlabel=r'$\mathrm{median}\ V_{\mathrm{los}}\,(\mathrm{km\,s}^{-1})$',
vmin=vmin,vmax=vmax)
bovy_plot.bovy_text(r'$\mathrm{typical\ uncertainty\!:}\ 3\,\mathrm{km\,s}^{-1}$',
bottom_left=True,size=18.)
bovy_plot.bovy_text(r'$|Z| < 250\,\mathrm{pc}$',top_right=True,size=18.)
bovy_plot.bovy_end_print(basesavefilename+'_XY.'+_EXT)
#R,phi
pix= pixelize_sample.pixelXY(data,rphi=True,
ymin=-22.5,ymax=37.5,
# dx=0.3,dy=2.)
# dx=3.,dy=20.)
dx=1.,dy=5.)
bovy_plot.bovy_print()
pix.plot('VHELIO_AVG',
zlabel=r'$\mathrm{median}\ V_{\mathrm{los}}\,(\mathrm{km\,s}^{-1})$',
vmin=vmin,vmax=vmax)
#bovy_plot.bovy_text(r'$|Z| < 250\,\mathrm{pc}$',bottom_left=True,size=18.)
bovy_plot.bovy_end_print(basesavefilename+'_RPHI.'+_EXT)
#Plot the dispersion / sqrt(n)
pix= pixelize_sample.pixelXY(data,
dx=1.,dy=1.)
bovy_plot.bovy_print()
pix.plot('VHELIO_AVG',
func=lambda x: 1.4826*numpy.median(numpy.fabs(x-numpy.median(x)))/numpy.sqrt(len(x)),
zlabel=r'$\delta\ V_{\mathrm{los}}\,(\mathrm{km\,s}^{-1})$',
vmin=0.,vmax=10.)
#bovy_plot.bovy_text(r'$|Z| < 250\,\mathrm{pc}$',bottom_left=True,size=18.)
bovy_plot.bovy_end_print(basesavefilename+'_RPHI_vlosunc.'+_EXT)
#Plot the dispersion
bovy_plot.bovy_print()
pix.plot('VHELIO_AVG',
func=lambda x: 1.4826*numpy.median(numpy.fabs(x-numpy.median(x))),
zlabel=r'$\mathrm{MAD}\ V_{\mathrm{los}}\,(\mathrm{km\,s}^{-1})$',
vmin=0.,vmax=40.)
#bovy_plot.bovy_text(r'$|Z| < 250\,\mathrm{pc}$',bottom_left=True,size=18.)
bovy_plot.bovy_end_print(basesavefilename+'_RPHI_vlosdisp.'+_EXT)
#Now plot the los velocity corrected for the Solar motion
#XY
vmin, vmax= -250., 250.
bovy_plot.bovy_print()
resv= pix.plot(lambda x: vlosgal(x),
zlabel=r'$\mathrm{median}\ V_{\mathrm{los,rot}}\,(\mathrm{km\,s}^{-1})$',
vmin=vmin,vmax=vmax,returnz=True)
bovy_plot.bovy_end_print(basesavefilename+'_Vrot_XY.'+_EXT)
pix= pixelize_sample.pixelXY(data,rphi=True,
ymin=-22.5,ymax=37.5,
dx=1.,dy=5.)
#R,phi
vmin, vmax= -250., 250.
bovy_plot.bovy_print()
resv= pix.plot(lambda x: vlosgal(x),
zlabel=r'$\mathrm{median}\ V_{\mathrm{los,rot}}\,(\mathrm{km\,s}^{-1})$',
vmin=vmin,vmax=vmax,returnz=True)
#Plot tangent point
rs= numpy.linspace(6.,8.,101)
bovy_plot.bovy_plot(rs,numpy.arccos(rs/8.)*180./numpy.pi,'k--',lw=2.,
overplot=True)
bovy_plot.bovy_plot(rs,-numpy.arccos(rs/8.)*180./numpy.pi,'k--',lw=2.,
overplot=True)
bovy_plot.bovy_end_print(basesavefilename+'_Vrot_RPHI.'+_EXT)
#Now plot the residuals wrt the Bovy et al. (2012) disk model
#R,phi
vmin, vmax= -20., 20.
bovy_plot.bovy_print()
resv= pix.plot(lambda x: dvlosgal(x,beta=0.,vc=220.),
zlabel=r'$\mathrm{median}\ \Delta V_{\mathrm{los}}\,(\mathrm{km\,s}^{-1})$',
vmin=vmin,vmax=vmax,returnz=True)
medindx= True-numpy.isnan(resv)
meanresv= numpy.mean(resv[medindx])
sigresv= numpy.std(resv[medindx])
bovy_plot.bovy_text(r'$\mathrm{Residual} = %.1f \pm %.1f / %i\,\mathrm{km\,s}^{-1}$' % (meanresv,sigresv,round(numpy.sqrt(numpy.sum(medindx)))),
top_left=True,size=16.)
bovy_plot.bovy_end_print(basesavefilename+'_dVBovy12_RPHI.'+_EXT)
#XY
pixXY= pixelize_sample.pixelXY(data,
xmin=5.,xmax=13,
ymin=-3.,ymax=4.,
dx=1.,dy=1.)
vmin, vmax= -20., 20.
bovy_plot.bovy_print()
pixXY.plot(lambda x: dvlosgal(x,beta=0.,vc=220.),
zlabel=r'$\mathrm{median}\ \Delta V_{\mathrm{los}}\,(\mathrm{km\,s}^{-1})$',
vmin=vmin,vmax=vmax,returnz=False)
medindx= True-numpy.isnan(resv)
meanresv= numpy.mean(resv[medindx])
sigresv= numpy.std(resv[medindx])
bovy_plot.bovy_text(r'$\mathrm{Residual} = %.1f \pm %.1f / %i\,\mathrm{km\,s}^{-1}$' % (meanresv,sigresv,round(numpy.sqrt(numpy.sum(medindx)))),
top_left=True,size=16.)
bovy_plot.bovy_end_print(basesavefilename+'_dVBovy12_XY.'+_EXT)
#Plot a histogram of the residuals
bovy_plot.bovy_print()
bovy_plot.bovy_hist(resv[medindx],color='k',bins=11,xrange=[-25.,25.],
yrange=[0.,15.],
histtype='step',normed=False,
xlabel=r'$\mathrm{median}\ \Delta V_{\mathrm{los}}\,(\mathrm{km\,s}^{-1})$')
xs= numpy.linspace(-25.,25.,1001)
bovy_plot.bovy_plot(xs,
50./11.*numpy.sum(medindx)/numpy.sqrt(2.*numpy.pi)/sigresv\
*numpy.exp(-0.5*(xs-meanresv)**2./sigresv**2.),
'k-',lw=2,overplot=True)
bovy_plot.bovy_end_print(basesavefilename+'_dVBovy12_hist.'+_EXT)
#Now plot the residuals wrt the Bovy et al. (2012) disk model w/ Vc-240 and standard solar motion
#R,phi
vmin, vmax= -20., 20.
bovy_plot.bovy_print()
resv= pix.plot(lambda x: dvlosgal(x,beta=0.,vc=240.,vtsun=252.),
zlabel=r'$\mathrm{median}\ \Delta V_{\mathrm{los}}\,(\mathrm{km\,s}^{-1})$',
vmin=vmin,vmax=vmax,returnz=True)
medindx= True-numpy.isnan(resv)
#bovy_plot.bovy_text(r'$\mathrm{Residual} = %.1f \pm %.1f / %i\,\mathrm{km\,s}^{-1}$' % (numpy.median(resv[medindx]),numpy.median(numpy.fabs(resv[medindx]-numpy.median(resv[medindx])))*1.4826,round(numpy.sqrt(numpy.sum(medindx)))),
# top_left=True,size=16.)
bovy_plot.bovy_end_print(basesavefilename+'_dVBovy12Vc240VsSBD_RPHI.'+_EXT)
#Now plot the residuals wrt the Bovy et al. (2012) disk model w/ Vc-220 and standard solar motion
#R,phi
vmin, vmax= -20., 20.
bovy_plot.bovy_print()
resv= pix.plot(lambda x: dvlosgal(x,beta=0.,vc=220.,vtsun=232.),
zlabel=r'$\mathrm{median}\ \Delta V_{\mathrm{los}}\,(\mathrm{km\,s}^{-1})$',
vmin=vmin,vmax=vmax,returnz=True)
medindx= True-numpy.isnan(resv)
# bovy_plot.bovy_text(r'$\mathrm{Residual} = %.1f \pm %.1f / %i\,\mathrm{km\,s}^{-1}$' % (numpy.median(resv[medindx]),numpy.median(numpy.fabs(resv[medindx]-numpy.median(resv[medindx])))*1.4826,round(numpy.sqrt(numpy.sum(medindx)))),
# top_left=True,size=16.)
bovy_plot.bovy_end_print(basesavefilename+'_dVBovy12Vc220VsSBD_RPHI.'+_EXT)
#FFT
dx= 1.
pix= pixelize_sample.pixelXY(data,
xmin=5.,xmax=13,
ymin=-3.,ymax=4.,
dx=dx,dy=dx)
resv= pix.plot(lambda x: dvlosgal(x),
zlabel=r'$\mathrm{median}\ \Delta V_{\mathrm{los}}\,(\mathrm{km\,s}^{-1})$',
vmin=vmin,vmax=vmax,returnz=True)
resvunc= pix.plot('VHELIO_AVG',
func=lambda x: 1.4826*numpy.median(numpy.fabs(x-numpy.median(x)))/numpy.sqrt(len(x)),
zlabel=r'$\delta\ V_{\mathrm{los}}\,(\mathrm{km\,s}^{-1})$',
vmin=0.,vmax=10.,returnz=True)
import psds
binsize= 1.5
psd2d= psds.power_spectrum(resv,
binsize=binsize,wavenumber=True)
#Simulate the noise
newresv= numpy.random.normal(size=resv.shape)*resvunc
newresvuni= numpy.random.normal(size=resv.shape)*5.
psd2dnoise= psds.power_spectrum(newresv,
binsize=binsize,wavenumber=True)
psd2duni= psds.power_spectrum(newresvuni,
binsize=binsize,wavenumber=True)
bovy_plot.bovy_print()
ks= psd2d[0][1:]/dx
bovy_plot.bovy_plot(ks,numpy.sqrt(psd2d[1]/numpy.prod(resv.shape)/16.\
/(ks[1]-ks[0]))[1:],'ko-',
xlabel=r'$k\,(\mathrm{kpc}^{-1})$',
ylabel=r'$\sqrt{P_k}\,(\mathrm{km\,s}^{-1}\,\mathrm{kpc}^{1/2})$',
xrange=[0.,1./dx/2.],
yrange=[0.,10.],
semilogy=False)
bovy_plot.bovy_plot(ks,numpy.sqrt(psd2dnoise[1]/numpy.prod(resv.shape)/16.\
/(ks[1]-ks[0]))[1:],
'-',color='0.5',overplot=True,lw=2.)
bovy_plot.bovy_plot(ks,numpy.sqrt(psd2duni[1]/numpy.prod(resv.shape)/16.\
/(ks[1]-ks[0]))[1:],
'-',color='b',overplot=True,lw=2.)
bovy_plot.bovy_end_print(basesavefilename+'_FFTPSD.'+_EXT)
return None
def plotCompareData(sample, savename, plotname):
locs = ['highb', 'outdisk', 'meddisk', 'indisk']
indices = [highbIndx, outDiskIndx, betwDiskIndx, inDiskIndx]
data_indices = [
data_highbIndx, data_outDiskIndx, data_betwDiskIndx, data_inDiskIndx
]
# Full prediction for numbers
Xs, pd = compareDataModel.predict_spacedist(bf_brexp,
numpy.array(locations),
copy.deepcopy(effsel_mar),
distmods,
type='tribrokenexpflare',
coord='dm')
for loc, index, data_index in zip(locs, indices, data_indices):
bovy_plot.bovy_print(axes_labelsize=20,
text_fontsize=20,
xtick_labelsize=20,
ytick_labelsize=20)
# High |b|
# Marshall is fiducial
Xs, pdt = compareDataModel.predict_spacedist(
bf_brexp,
numpy.array(locations)[index],
copy.deepcopy(effsel_mar)[index],
distmods,
type='tribrokenexpflare',
coord='dm')
if (sample.lower() == 'solar' and \
(loc.lower() == 'outdisk' or loc.lower() == 'highb')) \
or (sample.lower() == 'highfeh' and loc.lower() == 'highb'):
yrange = [
0., 1.6 * numpy.amax(pdt / numpy.sum(pdt) / (Xs[1] - Xs[0]))
]
else:
yrange = [
0., 1.4 * numpy.amax(pdt / numpy.sum(pdt) / (Xs[1] - Xs[0]))
]
bovy_plot.bovy_hist(ldata['RC_DM_H'][data_index],
histtype='stepfilled',
normed=True,
lw=_LW,
range=[7., 15.5],
bins=round(numpy.sqrt(numpy.sum(data_index)) * 2.),
yrange=yrange,
ec='k',
fc='0.75',
xlabel=r'$\mu$')
line_mar = bovy_plot.bovy_plot(Xs,
pdt / numpy.sum(pdt) / (Xs[1] - Xs[0]),
color='r',
lw=2. * _LW,
overplot=True,
zorder=12)
bovy_plot.bovy_text(r'$%i\%% = %i / %i\ \mathrm{stars}$' \
% (int(round(float(numpy.sum(data_index))/len(ldata)*100.)),
numpy.sum(data_index),
len(ldata))
+'\n'+r'$%i\%% = %i / %i\ \mathrm{predicted}$' \
% (int(round(numpy.sum(pdt)/numpy.sum(pd)*100.)),
numpy.sum(pdt)/numpy.sum(pd)*len(ldata),len(ldata)),
top_left=True,size=16.)
# Green
Xs, pdt = compareDataModel.predict_spacedist(
bf_brexp_g15,
numpy.array(locations)[index],
copy.deepcopy(effsel)[index],
distmods,
type='tribrokenexpflare',
coord='dm')
line_g15 = bovy_plot.bovy_plot(Xs,
pdt / numpy.sum(pdt) / (Xs[1] - Xs[0]),
color='b',
lw=_LW,
overplot=True,
zorder=13)
# Drimmel
Xs, pdt = compareDataModel.predict_spacedist(
bf_brexp_drim,
numpy.array(locations)[index],
copy.deepcopy(effsel_drim)[index],
distmods,
type='tribrokenexpflare',
coord='dm')
line_drim = bovy_plot.bovy_plot(Xs,
pdt / numpy.sum(pdt) / (Xs[1] - Xs[0]),
color='gold',
lw=_LW,
overplot=True,
zorder=12)
# Sale
Xs, pdt = compareDataModel.predict_spacedist(
bf_brexp_sale,
numpy.array(locations)[index],
copy.deepcopy(effsel_sale)[index],
distmods,
type='tribrokenexpflare',
coord='dm')
line_sale = bovy_plot.bovy_plot(Xs,
pdt / numpy.sum(pdt) / (Xs[1] - Xs[0]),
color='c',
lw=_LW,
overplot=True,
zorder=12)
# Zero
Xs, pdt = compareDataModel.predict_spacedist(
bf_brexp_zero,
numpy.array(locations)[index],
copy.deepcopy(effsel_zero)[index],
distmods,
type='tribrokenexpflare',
coord='dm')
line_zero = bovy_plot.bovy_plot(Xs,
pdt / numpy.sum(pdt) / (Xs[1] - Xs[0]),
color='k',
ls='-',
lw=_LW,
overplot=True,
zorder=10)
# Marshall + exp
Xs, pdt = compareDataModel.predict_spacedist(
bf_exp,
numpy.array(locations)[index],
copy.deepcopy(effsel_mar)[index],
distmods,
type='expplusconst',
coord='dm')
line_exp = bovy_plot.bovy_plot(Xs,
pdt / numpy.sum(pdt) / (Xs[1] - Xs[0]),
color='r',
lw=2 * _LW,
overplot=True,
zorder=10,
ls=':')
# Marshall + twoexp
Xs, pdt = compareDataModel.predict_spacedist(
bf_twoexp,
numpy.array(locations)[index],
copy.deepcopy(effsel_mar)[index],
distmods,
type='tribrokentwoexp',
coord='dm')
line_twoexp = bovy_plot.bovy_plot(Xs,
pdt / numpy.sum(pdt) /
(Xs[1] - Xs[0]),
color='r',
lw=2 * _LW,
overplot=True,
zorder=11,
ls='--')
if sample.lower() == 'lowlow' or sample.lower() == 'highalpha':
if loc.lower() == 'highb':
pyplot.annotate(r'$|b| > 10^\circ$', (0.5, 1.085),
xycoords='axes fraction',
horizontalalignment='center',
verticalalignment='top',
size=20.)
elif loc.lower() == 'indisk':
pyplot.annotate(r'$l < 70^\circ, |b| \leq 10^\circ$',
(0.5, 1.085),
xycoords='axes fraction',
horizontalalignment='center',
verticalalignment='top',
size=20.)
elif loc.lower() == 'meddisk':
pyplot.annotate(
r'$70^\circ \leq l \leq 140^\circ, |b| \leq 10^\circ$',
(0.5, 1.085),
xycoords='axes fraction',
horizontalalignment='center',
verticalalignment='top',
size=20.)
elif loc.lower() == 'outdisk':
pyplot.annotate(
r'$140^\circ < l < 250^\circ, |b| \leq 10^\circ$',
(0.5, 1.085),
xycoords='axes fraction',
horizontalalignment='center',
verticalalignment='top',
size=20.)
# Legend
if loc.lower() == 'meddisk':
pyplot.legend((line_mar[0], line_exp[0], line_twoexp[0]),
(r'$\mathrm{Marshall\ et\ al.\ (2006)}$' + '\n' +
r'$\mathrm{broken\ exp.\ w/\ flare}$',
r'$\mathrm{single\ exp.}$',
r'$\mathrm{broken\ exp.\ w/\ 2}\ h_Z$'),
loc='lower right',
bbox_to_anchor=(.66, .42),
numpoints=8,
prop={'size': 14},
frameon=False)
elif loc.lower() == 'outdisk':
pyplot.legend(
(line_g15[0], line_sale[0], line_drim[0], line_zero[0]),
(r'$\mathrm{Green\ et\ al.\ (2015)}$',
r'$\mathrm{Sale\ et\ al.\ (2014)}$',
r'$\mathrm{Drimmel\ et\ al.\ (2003)}$',
r'$\mathrm{zero\ extinction}$'),
loc='lower right',
bbox_to_anchor=(.66, .42),
numpoints=8,
prop={'size': 14},
frameon=False)
if loc.lower() == 'highb':
if sample.lower() == 'lowlow':
bovy_plot.bovy_text(r'$\mathrm{low\ [Fe/H]}$',
top_right=True,
size=18.)
elif sample.lower() == 'solar':
bovy_plot.bovy_text(r'$\mathrm{solar}$',
top_right=True,
size=18.)
elif sample.lower() == 'highfeh':
bovy_plot.bovy_text(r'$\mathrm{high\ [Fe/H]}$',
top_right=True,
size=18.)
elif sample.lower() == 'highalpha':
bovy_plot.bovy_text(r'$\mathrm{high}\ [\alpha/\mathrm{Fe]}$',
top_right=True,
size=18.)
bovy_plot.bovy_end_print(plotname.replace('LOC', loc))
return None
def plotPixelFitVel(options,args):
if options.sample.lower() == 'g':
if options.select.lower() == 'program':
raw= read_gdwarfs(_GDWARFFILE,logg=True,ebv=True,sn=options.snmin,
distfac=options.distfac)
else:
raw= read_gdwarfs(logg=True,ebv=True,sn=options.snmin,
distfac=options.distfac)
elif options.sample.lower() == 'k':
if options.select.lower() == 'program':
raw= read_kdwarfs(_KDWARFFILE,logg=True,ebv=True,sn=options.snmin,
distfac=options.distfac)
else:
raw= read_kdwarfs(logg=True,ebv=True,sn=options.snmin,
distfac=options.distfac)
if not options.bmin is None:
#Cut on |b|
raw= raw[(numpy.fabs(raw.b) > options.bmin)]
#print len(raw)
#Bin the data
binned= pixelAfeFeh(raw,dfeh=options.dfeh,dafe=options.dafe)
if options.tighten:
tightbinned= pixelAfeFeh(raw,dfeh=options.dfeh,dafe=options.dafe,
fehmin=-1.6,fehmax=0.5,afemin=-0.05,
afemax=0.55)
else:
tightbinned= binned
#Savefile
if os.path.exists(args[0]):#Load savefile
savefile= open(args[0],'rb')
fits= pickle.load(savefile)
savefile.close()
#Now plot
#Run through the pixels and gather
if options.type.lower() == 'afe' or options.type.lower() == 'feh' \
or options.type.lower() == 'fehafe' \
or options.type.lower() == 'afefeh':
plotthis= []
else:
plotthis= numpy.zeros((tightbinned.npixfeh(),tightbinned.npixafe()))
#ndata= 0
#maxndata= 0
for ii in range(tightbinned.npixfeh()):
for jj in range(tightbinned.npixafe()):
data= binned(tightbinned.feh(ii),tightbinned.afe(jj))
fehindx= binned.fehindx(tightbinned.feh(ii))#Map onto regular binning
afeindx= binned.afeindx(tightbinned.afe(jj))
if afeindx+fehindx*binned.npixafe() >= len(fits):
if options.type.lower() == 'afe' or options.type.lower() == 'feh' or options.type.lower() == 'fehafe' \
or options.type.lower() == 'afefeh':
continue
else:
plotthis[ii,jj]= numpy.nan
continue
thisfit= fits[afeindx+fehindx*binned.npixafe()]
if thisfit is None:
if options.type.lower() == 'afe' or options.type.lower() == 'feh' or options.type.lower() == 'fehafe' \
or options.type.lower() == 'afefeh':
continue
else:
plotthis[ii,jj]= numpy.nan
continue
if len(data) < options.minndata:
if options.type.lower() == 'afe' or options.type.lower() == 'feh' or options.type.lower() == 'fehafe' \
or options.type.lower() == 'afefeh':
continue
else:
plotthis[ii,jj]= numpy.nan
continue
#if len(data) > maxndata: maxndata= len(data)
#ndata+= len(data)
if options.model.lower() == 'hwr':
if options.type == 'sz':
plotthis[ii,jj]= numpy.exp(thisfit[1])
elif options.type == 'sz2':
plotthis[ii,jj]= numpy.exp(2.*thisfit[1])
elif options.type == 'hs':
plotthis[ii,jj]= numpy.exp(thisfit[4])
elif options.type == 'hsm':
plotthis[ii,jj]= numpy.exp(-thisfit[4])
elif options.type == 'pbad':
plotthis[ii,jj]= thisfit[0]
elif options.type == 'slopes':
plotthis[ii,jj]= thisfit[2]
elif options.type == 'slope':
plotthis[ii,jj]= thisfit[2]
elif options.type == 'chi2dof':
plotthis[ii,jj]= thisfit[6]
elif options.type.lower() == 'afe' \
or options.type.lower() == 'feh' \
or options.type.lower() == 'fehafe' \
or options.type.lower() == 'afefeh':
plotthis.append([tightbinned.feh(ii),
tightbinned.afe(jj),
numpy.exp(thisfit[1]),
numpy.exp(thisfit[3]),
len(data)])
#print ndata
#print maxndata
#Set up plot
#print numpy.nanmin(plotthis), numpy.nanmax(plotthis)
if options.type == 'sz':
if options.vr:
vmin, vmax= 40.,80.
zlabel= r'$\sigma_R(z = \langle z \rangle)\ [\mathrm{km\ s}^{-1}]$'
else:
vmin, vmax= 10.,60.
zlabel= r'$\sigma_z(z_{1/2})\ [\mathrm{km\ s}^{-1}]$'
elif options.type == 'sz2':
if options.vr:
vmin, vmax= 40.**2.,80.**2.
zlabel= r'$\sigma_R^2(z= \langle z \rangle)\ [\mathrm{km\ s}^{-1}]$'
else:
vmin, vmax= 15.**2.,50.**2.
zlabel= r'$\sigma_z^2(z= \langle z \rangle)\ [\mathrm{km\ s}^{-1}]$'
elif options.type == 'hs':
if options.vr:
vmin, vmax= 3.,25.
zlabel= r'$R_\sigma\ [\mathrm{kpc}]$'
else:
vmin, vmax= 3.,15.
zlabel= r'$h_\sigma\ [\mathrm{kpc}]$'
elif options.type == 'hsm':
if options.vr:
vmin, vmax= 0.,0.3
zlabel= r'$R^{-1}_\sigma\ [\mathrm{kpc}^{-1}]$'
else:
vmin, vmax= 0.,0.3
zlabel= r'$R^{-1}_\sigma\ [\mathrm{kpc}^{-1}]$'
elif options.type == 'slope':
vmin, vmax= -5.,5.
zlabel= r'$\frac{\mathrm{d} \sigma_z}{\mathrm{d} z}(z_{1/2})\ [\mathrm{km\ s}^{-1}\ \mathrm{kpc}^{-1}]$'
elif options.type == 'pbad':
vmin, vmax= 0.,0.1
zlabel= r'$P_{\mathrm{bad}}$'
elif options.type == 'chi2dof':
vmin, vmax= 0.5,1.5
zlabel= r'$\chi^2/\mathrm{dof}$'
elif options.type == 'afe':
vmin, vmax= 0.05,.4
zlabel=r'$[\alpha/\mathrm{Fe}]$'
elif options.type == 'feh':
vmin, vmax= -1.5,0.
zlabel=r'$[\mathrm{Fe/H}]$'
elif options.type == 'fehafe':
vmin, vmax= -.7,.7
zlabel=r'$[\mathrm{Fe/H}]-[\mathrm{Fe/H}]_{1/2}([\alpha/\mathrm{Fe}])$'
elif options.type == 'afefeh':
vmin, vmax= -.15,.15
zlabel=r'$[\alpha/\mathrm{Fe}]-[\alpha/\mathrm{Fe}]_{1/2}([\mathrm{Fe/H}])$'
if options.tighten:
xrange=[-1.6,0.5]
yrange=[-0.05,0.55]
else:
xrange=[-2.,0.6]
yrange=[-0.1,0.6]
if options.type.lower() == 'afe' or options.type.lower() == 'feh' \
or options.type.lower() == 'fehafe' \
or options.type.lower() == 'afefeh':
print "Update!! Never used until now (afe etc. type fitting is in plotsz2hz"
return None
bovy_plot.bovy_print(fig_height=3.87,fig_width=5.)
#Gather hR and hz
hz, hr,afe, feh, ndata= [], [], [], [], []
for ii in range(len(plotthis)):
hz.append(plotthis[ii][2])
hr.append(plotthis[ii][3])
afe.append(plotthis[ii][1])
feh.append(plotthis[ii][0])
ndata.append(plotthis[ii][4])
hz= numpy.array(hz)
hr= numpy.array(hr)
afe= numpy.array(afe)
feh= numpy.array(feh)
ndata= numpy.array(ndata)
#Process ndata
ndata= ndata**.5
ndata= ndata/numpy.median(ndata)*35.
#ndata= numpy.log(ndata)/numpy.log(numpy.median(ndata))
#ndata= (ndata-numpy.amin(ndata))/(numpy.amax(ndata)-numpy.amin(ndata))*25+12.
if options.type.lower() == 'afe':
plotc= afe
elif options.type.lower() == 'feh':
plotc= feh
elif options.type.lower() == 'afefeh':
#Go through the bins to determine whether feh is high or low for this alpha
plotc= numpy.zeros(len(afe))
for ii in range(tightbinned.npixfeh()):
fehbin= ii
data= tightbinned.data[(tightbinned.data.feh > tightbinned.fehedges[fehbin])\
*(tightbinned.data.feh <= tightbinned.fehedges[fehbin+1])]
medianafe= numpy.median(data.afe)
for jj in range(len(afe)):
if feh[jj] == tightbinned.feh(ii):
plotc[jj]= afe[jj]-medianafe
else:
#Go through the bins to determine whether feh is high or low for this alpha
plotc= numpy.zeros(len(feh))
for ii in range(tightbinned.npixafe()):
afebin= ii
data= tightbinned.data[(tightbinned.data.afe > tightbinned.afeedges[afebin])\
*(tightbinned.data.afe <= tightbinned.afeedges[afebin+1])]
medianfeh= numpy.median(data.feh)
for jj in range(len(feh)):
if afe[jj] == tightbinned.afe(ii):
plotc[jj]= feh[jj]-medianfeh
yrange= [150,1200]
xrange= [1.2,5.]
bovy_plot.bovy_plot(hr,hz,s=ndata,c=plotc,
cmap='jet',
ylabel=r'$\mathrm{vertical\ scale\ height\ [pc]}$',
xlabel=r'$\mathrm{radial\ scale\ length\ [kpc]}$',
clabel=zlabel,
xrange=xrange,yrange=yrange,
vmin=vmin,vmax=vmax,
scatter=True,edgecolors='none',
colorbar=True)
elif options.type.lower() == 'slopes':
bovy_plot.bovy_print()
bovy_plot.bovy_hist(plotthis.flatten(),
range=[-5.,5.],
bins=11,
histtype='step',
color='k',
xlabel=r'$\sigma_z(z)\ \mathrm{slope\ [km\ s}^{-1}\ \mathrm{kpc}^{-1}]$')
else:
bovy_plot.bovy_print()
bovy_plot.bovy_dens2d(plotthis.T,origin='lower',cmap='jet',
interpolation='nearest',
xlabel=r'$[\mathrm{Fe/H}]$',
ylabel=r'$[\alpha/\mathrm{Fe}]$',
zlabel=zlabel,
xrange=xrange,yrange=yrange,
vmin=vmin,vmax=vmax,
contours=False,
colorbar=True,shrink=0.78)
if options.observed:
bovy_plot.bovy_text(r'$\mathrm{observed}$',
top_right=True,size=18.)
bovy_plot.bovy_end_print(options.plotfile)
return None
def plot_fake_data(parser):
"""
NAME:
plot_fake_data
PURPOSE:
plot the fake data created by create_fake_data
INPUT:
parser - from optparse
OUTPUT:
stuff as specified by the options
HISTORY:
2011-04-20 - Written - Bovy (NYU)
"""
(options,args)= parser.parse_args()
if len(args) == 0:
parser.print_help()
sys.exit(-1)
#Set up DF
dfc= dehnendf(beta=options.beta,profileParams=(options.rd,options.rs,options.so),correct=True,niter=20)
#Open pickle
picklefile= open(args[0],'rb')
out= pickle.load(picklefile)
picklefile.close()
if not options.vlosdname is None:
#Plot distribution of vlos vs. d
vloss=nu.array([o.vlos(obs=[1.,0.,0.,0.,1.,0.],ro=1.,vo=1.) \
for o in out]).flatten()
ds= nu.array([o.dist(obs=[1.,0.,0.],ro=1.) \
for o in out]).flatten()
#Also calculate the expected relation
ntheory= 1001
dx= nu.linspace(0.,2.,ntheory)
vtheory= nu.zeros(ntheory)
l= options.los*_DEGTORAD
for ii in range(ntheory):
R= nu.sqrt(1.+dx[ii]**2.-2.*dx[ii]*nu.cos(l))
if 1./nu.cos(l) < dx[ii] and nu.cos(l) > 0.:
phi= nu.pi-m.asin(dx[ii]/R*nu.sin(l))
else:
phi= m.asin(dx[ii]/R*nu.sin(l))
vtheory[ii]= (R**options.beta-dfc.asymmetricdrift(R))*m.sin(phi+l)-m.sin(l) #last term is the LSR
bovy_plot.bovy_print()
bovy_plot.scatterplot(ds,vloss,'.',bins=options.scatterbins,
xlabel=r'$d / R_0$',
ylabel=r'$v_{los} / v_0$')
bovy_plot.bovy_plot(dx,vtheory,overplot=True)
bovy_plot.bovy_text(r'$l = %i^\circ$' % round(options.los),
top_left=True)
bovy_plot.bovy_end_print(options.vlosdname)
if not options.vtname is None:
vts= nu.array([o[1]-1. for o in out]).flatten() #subtract vc
#Calculate expected
ntheory= 1001
vx= nu.linspace(-1.,1.,ntheory)
y= nu.zeros(ntheory)
for ii in range(ntheory):
y[ii]= dfc(Orbit([1.,0.,vx[ii]+1.]))
y/= nu.sum(y)*(vx[1]-vx[0])
bovy_plot.bovy_print()
bovy_plot.bovy_hist(vts,bins=options.histbins,normed=True,
xlabel=r'$v_T / v_0 - 1$')
bovy_plot.bovy_plot(vx,y,overplot=True)
bovy_plot.bovy_end_print(options.vtname)
if not options.vrname is None:
vrs= nu.array([o[0] for o in out]).flatten() #subtract vc
#Calculate expected
ntheory= 1001
vx= nu.linspace(-1.,1.,ntheory)
y= nu.zeros(ntheory)
vT= 1.-dfc.asymmetricdrift(1.)
for ii in range(ntheory):
y[ii]= dfc(Orbit([1.,vx[ii],vT]))
y/= nu.sum(y)*(vx[1]-vx[0])
bovy_plot.bovy_print()
bovy_plot.bovy_hist(vrs,bins=options.histbins,normed=True,
xlabel=r'$v_R / v_0$')
bovy_plot.bovy_plot(vx,y,overplot=True)
bovy_plot.bovy_end_print(options.vrname)
if not options.vrvtname is None:
vts= nu.array([o[1]-1. for o in out]).flatten() #subtract vc
vrs= nu.array([o[0] for o in out]).flatten() #subtract vc
bovy_plot.bovy_print()
bovy_plot.scatterplot(vrs,vts,'.',bins=options.scatterbins,
xlabel=r'$v_R / v_0$',
ylabel=r'$v_T / v_0 - 1$')
bovy_plot.bovy_end_print(options.vrvtname)
def plot_mapflarepdf(savename, plotname):
# Load the samples
with open('../mapfits/tribrokenexpflare.sav', 'rb') as savefile:
bf = numpy.array(pickle.load(savefile))
samples = numpy.array(pickle.load(savefile))
maps = define_rcsample.MAPs()
# Loop through the low-alpha MAPs and compute the XD decomposition
if 'lowalpha' in savename:
plotmaps = [9, 16, 23, 29, 36, 43, 50, 57, 64, 71]
else:
plotmaps = [19, 26, 32, 39, 45]
if not os.path.exists(savename):
ngauss = 2
allxamp = numpy.empty((len(plotmaps), ngauss))
allxmean = numpy.empty((len(plotmaps), ngauss, 1))
allxcovar = numpy.empty((len(plotmaps), ngauss, 1, 1))
cnt = 0
for ii, map in enumerate(maps.map()):
if not ii in plotmaps: continue
print ii
# Fit PDFs with XD
xamp = numpy.array([0.45, 0.5])
xmean = numpy.array([
numpy.mean(samples[ii, 4]) +
numpy.random.normal() * numpy.std(samples[ii, 4]),
numpy.mean(samples[ii, 4]) +
numpy.random.normal() * numpy.std(samples[ii, 4])
])[:, numpy.newaxis]
xcovar = numpy.reshape(
numpy.tile(numpy.var(samples[ii, 4]), (2, 1)), (2, 1, 1))
XD.extreme_deconvolution(samples[ii, 4][:, numpy.newaxis],
numpy.zeros((len(samples[ii, 4]), 1)),
xamp, xmean, xcovar)
allxamp[cnt] = xamp
allxmean[cnt] = xmean
allxcovar[cnt] = xcovar
cnt += 1
save_pickles(savename, allxamp, allxmean, allxcovar)
else:
with open(savename, 'rb') as savefile:
allxamp = pickle.load(savefile)
allxmean = pickle.load(savefile)
allxcovar = pickle.load(savefile)
# Now plot
cmap = cm.coolwarm
xrange = [-0.37, 0.25]
if 'lowalpha' in savename:
# xrange= [-0.4,0.2]
yrange = [0., 30.]
combDiv = 2.
colorFunc = lambda x: cmap((x + 0.6) * 0.95 / 0.9 + 0.05)
else:
# xrange= [-0.3,0.3]
yrange = [0., 13.5]
colorFunc = lambda x: cmap((x + 0.5) * 0.95 / 0.5 + 0.05)
combDiv = 1.5
overplot = False
plotXDFit = True
cnt = 0
bovy_plot.bovy_print(axes_labelsize=18,
text_fontsize=18,
xtick_labelsize=14,
ytick_labelsize=14)
for ii, map in enumerate(maps.map()):
if not ii in plotmaps: continue
tfeh = round(numpy.median(map['FE_H']) * 20.) / 20.
if tfeh == 0.25: tfeh = 0.3
if tfeh == -0.1: tfeh = -0.1
bovy_plot.bovy_hist(
samples[ii, 4],
range=xrange,
bins=51,
overplot=overplot,
yrange=yrange,
histtype='step',
normed=True,
zorder=2,
color=colorFunc(tfeh),
xlabel=r'$R_{\mathrm{flare}}^{-1}\,(\mathrm{kpc}^{-1})$')
if plotXDFit:
txs = numpy.linspace(xrange[0], xrange[1], 1001)
pyplot.plot(
txs,
1. / numpy.sqrt(2. * numpy.pi) *
(allxamp[cnt, 0] / numpy.sqrt(allxcovar[cnt, 0, 0, 0]) *
numpy.exp(-0.5 * (txs - allxmean[cnt, 0, 0])**2. /
allxcovar[cnt, 0, 0, 0]) +
allxamp[cnt, 1] / numpy.sqrt(allxcovar[cnt, 1, 0, 0]) *
numpy.exp(-0.5 * (txs - allxmean[cnt, 1, 0])**2. /
allxcovar[cnt, 1, 0, 0])),
color=colorFunc(tfeh),
zorder=1)
overplot = True
cnt += 1
txs = numpy.linspace(xrange[0], xrange[1], 1001)
comb = numpy.ones_like(txs)
for ii in range(len(plotmaps)):
comb *= 1. / numpy.sqrt(2. * numpy.pi) * (
allxamp[ii, 0] / numpy.sqrt(allxcovar[ii, 0, 0, 0]) *
numpy.exp(-0.5 *
(txs - allxmean[ii, 0, 0])**2. / allxcovar[ii, 0, 0, 0])
+ allxamp[ii, 1] / numpy.sqrt(allxcovar[ii, 1, 0, 0]) *
numpy.exp(-0.5 *
(txs - allxmean[ii, 1, 0])**2. / allxcovar[ii, 1, 0, 0]))
comb /= numpy.sum(comb) * (txs[1] - txs[0])
pyplot.plot(txs, comb / combDiv, 'k-', lw=2., zorder=20)
pyplot.plot([0., 0.], [0., 50.], 'k--', lw=1.5, zorder=0)
t = pyplot.text(
xrange[0] + 0.25 * (xrange[1] - xrange[0]) + 0.03 *
('highalpha' in savename),
0.8 * yrange[1],
r'$R_{\mathrm{flare}}^{-1} = %.2f \pm %.2f\,\mathrm{kpc}^{-1}$' %
(numpy.sum(comb * txs) / numpy.sum(comb),
numpy.sqrt(
numpy.sum(comb * txs**2.) / numpy.sum(comb) -
(numpy.sum(comb * txs) / numpy.sum(comb))**2.)),
size=18.)
t.set_bbox(dict(color='w', edgecolor='none'))
if 'lowalpha' in savename:
bovy_plot.bovy_text(
r'$\mathrm{low-}[\alpha/\mathrm{Fe}]\ \mathrm{MAPs}$',
top_left=True,
size=16.)
else:
bovy_plot.bovy_text(
r'$\mathrm{high-}[\alpha/\mathrm{Fe}]\ \mathrm{MAPs}$',
top_left=True,
size=16.)
bovy_plot.bovy_end_print(plotname)
return None
profileParams=(1./3.,1.,0.4))
vs= dfc.sampleVRVT(1.,n=100000,nsigma=5)
vts04= vs[:,1].flatten()
save_pickles(savefilename,vts01,vts02,vts04)
xs= numpy.linspace(-1.,2.,1001)
bf= optimize.fmin(optm,[.8,0.4,-0.3],(vts04,))
print bf[2]
ys= skewnormal(xs,bf[0],numpy.exp(bf[1]),bf[2])
bovy_plot.bovy_print()
bovy_plot.bovy_plot(xs,ys,'k-',zorder=2,
xlabel=r'$v_T / v_0$',
ylabel=r'$\mathrm{normalized\ distribution}$',
xrange=[-.5,1.5],
yrange=[0.,6.])
bovy_plot.bovy_hist(vts04,bins=101,normed=True,
overplot=True,
histtype='step',
color='k')
bf= optimize.fmin(optm,[9.,0.2,-0.1],(vts02,))
print bf[2]
ys= skewnormal(xs,bf[0],numpy.exp(bf[1]),bf[2])
bovy_plot.bovy_hist(vts02,bins=101,normed=True,
histtype='step',
overplot=True,
color='k')
bovy_plot.bovy_plot(xs,ys,'k-',overplot=True)
bf= optimize.fmin(optm,[1.,0.1,0.1],(vts01,))
print bf[2]
ys= skewnormal(xs,bf[0],numpy.exp(bf[1]),bf[2])
bovy_plot.bovy_hist(vts01,bins=101,normed=True,
histtype='step',
overplot=True,
def plot_stream_times(plotfilename):
#Read stream
data= numpy.loadtxt(os.path.join(_STREAMSNAPAADIR,
'gd1_evol_hitres_aa_01312.dat'),
delimiter=',')
#Calculate times at which stars were stripped, angles
thetar= data[:,6]
thetar= (numpy.pi+(thetar-numpy.median(thetar))) % (2.*numpy.pi)
indx= numpy.fabs(thetar-numpy.pi) > (5.*numpy.median(numpy.fabs(thetar-numpy.median(thetar))))
thetar= thetar[indx]
thetap= data[:,7]
thetap= (numpy.pi+(thetap-numpy.median(thetap))) % (2.*numpy.pi)
thetap= thetap[indx]
thetaz= data[:,8]
thetaz= (numpy.pi+(thetaz-numpy.median(thetaz))) % (2.*numpy.pi)
thetaz= thetaz[indx]
#center around 0 (instead of pi)
thetar-= numpy.pi
thetap-= numpy.pi
thetaz-= numpy.pi
#Frequencies
Or= data[:,3]
Op= data[:,4]
Oz= data[:,5]
dOr= Or[indx]-numpy.median(Or)
dOp= Op[indx]-numpy.median(Op)
dOz= Oz[indx]-numpy.median(Oz)
#Times
dangle= numpy.vstack((thetar,thetap,thetaz))
dO= numpy.vstack((dOr,dOp,dOz))*bovy_conversion.freq_in_Gyr(220.,8.)
dts= numpy.sum(dO*dangle,axis=0)/numpy.sum(dO**2.,axis=0)
if 'hist' in plotfilename:
bovy_plot.bovy_print()
bovy_plot.bovy_hist(dts,range=[0.,6.],bins=13,
xlabel=r'$t_s\ (\mathrm{Gyr})$',
histtype='step',color='k',normed=True,lw=2.)
bovy_plot.bovy_hist(dts,range=[0.,6.],bins=61,normed=True,
overplot=True,ls='dotted',histtype='step',
color='0.5',lw=2.)
includeorbit= False #bc pericenter passage doesn't seem to work
if includeorbit:
npts= 10001
pot= potential.LogarithmicHaloPotential(normalize=1.,q=0.9)
ts= numpy.linspace(0.,5./bovy_conversion.time_in_Gyr(220.,8.),
npts)
#Calculate progenitor orbit around this point
pox= numpy.median(data[:,1])
poy= numpy.median(data[:,3])
poz= numpy.median(data[:,2])
povx= numpy.median(data[:,4])
povy= numpy.median(data[:,6])
povz= numpy.median(data[:,5])
pR,pphi,pZ= bovy_coords.rect_to_cyl(pox,poy,poz)
pvR,pvT,pvZ= bovy_coords.rect_to_cyl_vec(povx,povy,povz,pR,
pphi,pZ,cyl=True)
o= Orbit([pR/8.,-pvR/220.,-pvT/220.,pZ/8.,-pvZ/220.,pphi])
o.integrate(ts,pot)
rs= numpy.sqrt(o.R(ts)**2.+o.z(ts)**2.)
#Find local minima
periIndx= numpy.r_[True, rs[1:] < rs[:-1]] & numpy.r_[rs[:-1] < rs[1:], True]
for ii in range(numpy.sum(periIndx)):
bovy_plot.bovy_plot([ts[periIndx][ii]*bovy_conversion.time_in_Gyr(220.,8.),
ts[periIndx][ii]*bovy_conversion.time_in_Gyr(220.,8.)],
[0.,1.],overplot=True,ls='--',lw=1.,
color='0.5')
elif 'dO' in plotfilename:
bovy_plot.bovy_print()
bovy_plot.bovy_plot(dts,
numpy.sqrt(numpy.sum(dO**2.,axis=0)),
'k.',
xrange=[0.,6.],
yrange=[0.1,0.35],
xlabel=r'$t_s\ (\mathrm{Gyr})$',
ylabel=r'$|\Delta \mathbf{\Omega}|\ (\mathrm{Gyr}^{-1})$')
elif 'da' in plotfilename:
bovy_plot.bovy_print()
bovy_plot.bovy_plot(dts,
numpy.sqrt(numpy.sum(dangle**2.,axis=0)),
'k.',
xrange=[0.,6.],
yrange=[0.,1.5],
xlabel=r'$t_s\ (\mathrm{Gyr})$',
ylabel=r'$|\Delta \boldsymbol\theta|$')
bovy_plot.bovy_end_print(plotfilename)
return None
def calc_es():
savefilename = 'myes.sav'
if os.path.exists(savefilename):
savefile = open(savefilename, 'rb')
mye = pickle.load(savefile)
e = pickle.load(savefile)
savefile.close()
else:
#Read data
dialect = csv.excel
dialect.skipinitialspace = True
reader = csv.reader(open('../data/Dierickx-etal-tab2.txt', 'r'),
delimiter=' ',
dialect=dialect)
vxvs = []
es = []
vphis = []
vxs = []
vys = []
vzs = []
ls = []
for row in reader:
thisra = float(row[3])
thisdec = float(row[4])
thisd = float(row[17]) / 1000.
thispmra = float(row[13])
thispmdec = float(row[15])
thisvlos = float(row[11])
thise = float(row[26])
vxvs.append(
[thisra, thisdec, thisd, thispmra, thispmdec, thisvlos])
es.append(thise)
vphis.append(float(row[25]))
vxs.append(float(row[19]))
vys.append(float(row[21]))
vzs.append(float(row[23]))
ls.append(float(row[5]))
vxvv = nu.array(vxvs)
e = nu.array(es)
vphi = nu.array(vphis)
vx = nu.array(vxs)
vy = nu.array(vys)
vz = nu.array(vzs)
l = nu.array(ls)
#Define potential
lp = LogarithmicHaloPotential(normalize=1.)
mp = MiyamotoNagaiPotential(a=0.5, b=0.0375, amp=1., normalize=.6)
np = NFWPotential(a=4.5, normalize=.35)
hp = HernquistPotential(a=0.6 / 8, normalize=0.05)
ts = nu.linspace(0., 20., 10000)
mye = nu.zeros(len(e))
for ii in range(len(e)):
#Integrate the orbit
o = Orbit(vxvv[ii, :], radec=True, vo=220., ro=8.)
o.integrate(ts, lp)
mye[ii] = o.e()
#Save
savefile = open(savefilename, 'wb')
pickle.dump(mye, savefile)
pickle.dump(e, savefile)
savefile.close()
#plot
plot.bovy_print()
plot.bovy_plot(nu.array([0., 1.]),
nu.array([0., 1.]),
'k-',
xlabel=r'$\mathrm{Dierickx\ et\ al.}\ e$',
ylabel=r'$\mathrm{galpy}\ e$')
plot.bovy_plot(e, mye, 'k,', overplot=True)
plot.bovy_end_print('myee.png')
plot.bovy_print()
plot.bovy_hist(e, bins=30, xlabel=r'$\mathrm{Dierickx\ et\ al.}\ e$')
plot.bovy_end_print('ehist.png')
plot.bovy_print()
plot.bovy_hist(mye, bins=30, xlabel=r'$\mathrm{galpy}\ e$')
plot.bovy_end_print('myehist.png')
gauxd_mean[irad, :] = initmean[sortindx, 0]
gauxd_std[irad, :] = np.sqrt(initcovar[sortindx, 0, 0])
gauxd_rr[irad] = rr
# for plot
if FigGauMod == True:
xs = np.linspace(-50., 50., 1001)
ys = np.sum(np.atleast_2d( \
initamp/np.sqrt(initcovar[:,0,0])).T\
*np.exp(-0.5*(xs-np.atleast_2d(initmean[:,0]).T)**2. \
/np.atleast_2d(initcovar[:,0,0]).T),axis=0)\
/np.sqrt(2.*np.pi)
_ = bovy_plot.bovy_hist(vals,
bins=41,
range=[-50., 50.],
histtype='step',
lw=2.,
normed=True,
overplot=True)
plt.plot(xs, ys)
for ii in range(ngauss):
ys = (initamp[ii]/np.sqrt(initcovar[ii,0,0]))\
*np.exp(-0.5*(xs-initmean[ii,0])**2. \
/initcovar[ii,0,0])/np.sqrt(2.*np.pi)
plt.plot(xs, ys)
# print("Combined <v^2>, sqrt(<v^2>):",combined_sig2( \
# initamp,initmean[:,0],initcovar[:,0,0]),
# np.sqrt(combined_sig2(initamp,initmean[:,0],initcovar[:,0,0])))
plt.xlabel(r'$V\,(\mathrm{km\,s}^{-1})$')
filename = 'samp' + str(isamp) + 'v' + str(
ivel) + 'r' + cirad + '.png'
def plotVelComparisonDFMulti(options,args):
#Read data etc.
print "Reading the data ..."
raw= read_rawdata(options)
#Bin the data
binned= pixelAfeFeh(raw,dfeh=options.dfeh,dafe=options.dafe)
#Map the bins with ndata > minndata in 1D
fehs, afes= [], []
for ii in range(len(binned.fehedges)-1):
for jj in range(len(binned.afeedges)-1):
data= binned(binned.feh(ii),binned.afe(jj))
if len(data) < options.minndata:
continue
#print binned.feh(ii), binned.afe(jj), len(data)
fehs.append(binned.feh(ii))
afes.append(binned.afe(jj))
nabundancebins= len(fehs)
fehs= numpy.array(fehs)
afes= numpy.array(afes)
gafes, gfehs, left_legend= getMultiComparisonBins(options)
M= len(gfehs)
if options.andistances:
distancefacs= numpy.zeros_like(fehs)
gdistancefacs= numpy.zeros_like(gfehs)
for jj in range(M):
#Find pop corresponding to this bin
ii= numpy.argmin((gfehs[jj]-fehs)**2./0.1+(gafes[jj]-afes)**2./0.0025)
print ii
#Get the relevant data
data= binned(fehs[ii],afes[ii])
distancefacs[ii]= AnDistance.AnDistance(data.dered_g-data.dered_r,
data.feh)
gdistancefacs[jj]= distancefacs[ii]
options.fixdm= numpy.log10(distancefacs[ii])*5.
#Apply distance factor to the data
newraw= read_rawdata(options)
newbinned= pixelAfeFeh(newraw,dfeh=options.dfeh,dafe=options.dafe)
thisdataIndx= binned.callIndx(fehs[ii],afes[ii])
binned.data.xc[thisdataIndx]= newbinned.data.xc[thisdataIndx]
binned.data.yc[thisdataIndx]= newbinned.data.yc[thisdataIndx]
binned.data.zc[thisdataIndx]= newbinned.data.zc[thisdataIndx]
binned.data.plate[thisdataIndx]= newbinned.data.plate[thisdataIndx]
binned.data.dered_r[thisdataIndx]= newbinned.data.dered_r[thisdataIndx]
else:
distancefacs=numpy.ones_like(fehs)
gdistancefacs=numpy.ones_like(gfehs)
##########POTENTIAL PARAMETERS####################
potparams1= numpy.array([numpy.log(2.5/8.),options.fixvc/220.,
numpy.log(400./8000.),0.2,0.])
if options.group.lower() == 'aenhanced':
potparams2= numpy.array([numpy.log(2.8/8.),options.fixvc/220.,
numpy.log(400./8000.),0.266666666,0.])
potparams3= numpy.array([numpy.log(2.8/8.),options.fixvc/220.,
numpy.log(400./8000.),0.8,0.])
elif options.group.lower() == 'aintermediate':
potparams2= numpy.array([numpy.log(3.0/8.),options.fixvc/220.,
numpy.log(400./8000.),0.3333333333333,0.])
potparams3= numpy.array([numpy.log(3.0/8.),options.fixvc/220.,
numpy.log(400./8000.),0.933333333333,0.])
elif options.group.lower() == 'apoor':
potparams2= numpy.array([numpy.log(2.6/8.),options.fixvc/220.,
numpy.log(400./8000.),0.4,0.])
potparams3= numpy.array([numpy.log(2.6/8.),options.fixvc/220.,
numpy.log(400./8000.),1.0,0.])
options.potential= 'dpdiskplhalofixbulgeflatwgasalt'
#Check whether fits exist, if not, pop
removeBins= numpy.ones(M,dtype='bool')
for jj in range(M):
#Find pop corresponding to this bin
pop= numpy.argmin((gfehs[jj]-fehs)**2./0.1+(gafes[jj]-afes)**2./0.0025)
#Load savefile
if not options.init is None:
#Load initial parameters from file
savename= options.init
spl= savename.split('.')
newname= ''
for ll in range(len(spl)-1):
newname+= spl[ll]
if not ll == len(spl)-2: newname+= '.'
newname+= '_%i.' % pop
newname+= spl[-1]
if not os.path.exists(newname):
removeBins[jj]= False
else:
raise IOError("base filename not specified ...")
if numpy.sum(removeBins) == 0:
raise IOError("None of the group bins have been fit ...")
elif numpy.sum(removeBins) < M:
#Some bins have not been fit yet, and have to be remove
gfehs= list((numpy.array(gfehs))[removeBins])
gafes= list((numpy.array(gafes))[removeBins])
print "Only using %i bins out of %i ..." % (numpy.sum(removeBins),M)
M= len(gfehs)
data= []
zs= []
velps= numpy.zeros((len(binned.data),options.nv))
velps[:,:]= numpy.nan
velps2= numpy.zeros((len(binned.data),options.nv))
velps2[:,:]= numpy.nan
velps3= numpy.zeros((len(binned.data),options.nv))
velps3[:,:]= numpy.nan
cumulndata= 0
if options.type.lower() == 'vz':
if options.group == 'aenhanced':
vs= numpy.linspace(-180.,180.,options.nv)
xrange=[-180.,180.]
bins= 39
elif options.group == 'aintermediate':
vs= numpy.linspace(-150.,150.,options.nv)
xrange=[-150.,150.]
bins= 33
else: # options.group == 'aenhanced':
vs= numpy.linspace(-120.,120.,options.nv)
xrange=[-140.,140.]
bins= 26
xlabel=r'$V_Z\ (\mathrm{km\,s}^{-1})$'
elif options.type.lower() == 'vr':
if options.group == 'aenhanced':
vs= numpy.linspace(-220.,220.,options.nv)
xrange=[-220.,220.]
bins= 39
else: # options.group == 'aenhanced':
vs= numpy.linspace(-150.,150.,options.nv)
xrange=[-150.,150.]
bins= 26
xlabel=r'$V_R\ (\mathrm{km\,s}^{-1})$'
elif options.type.lower() == 'vt':
if options.group == 'aenhanced':
vs= numpy.linspace(0.01,350.,options.nv)
xrange=[0.,350.]
bins= 39
else: # options.group == 'aenhanced':
vs= numpy.linspace(0.01,350.,options.nv)
xrange=[0.,350.]
bins= 39
xlabel=r'$V_T\ (\mathrm{km\,s}^{-1})$'
alts= True
if not options.multi is None:
#Generate list of temporary files
tmpfiles= []
for jj in range(M):
tfile, tmpfile= tempfile.mkstemp()
os.close(tfile) #Close because it's open
tmpfiles.append(tmpfile)
try:
dummy= multi.parallel_map((lambda x: run_calc_model_multi(x,M,gfehs,gafes,fehs,afes,options,
vs,
potparams1,potparams2,potparams3,
distancefacs,
binned,alts,True,tmpfiles)),
range(M),
numcores=numpy.amin([M,
multiprocessing.cpu_count(),
options.multi]))
#Now read all of the temporary files
for jj in range(M):
tmpfile= open(tmpfiles[jj],'rb')
tvelps= pickle.load(tmpfile)
if tvelps is None:
continue
tvelps2= pickle.load(tmpfile)
tvelps3= pickle.load(tmpfile)
data.extend(pickle.load(tmpfile))
zs.extend(pickle.load(tmpfile))
tndata= pickle.load(tmpfile)
velps[cumulndata:cumulndata+tndata,:]= tvelps
velps2[cumulndata:cumulndata+tndata,:]= tvelps2
velps3[cumulndata:cumulndata+tndata,:]= tvelps3
cumulndata+= tndata
tmpfile.close()
finally:
for jj in range(M):
os.remove(tmpfiles[jj])
else:
for jj in range(M):
try:
tvelps, tvelps2, tvelps3, tdata, tzs, tndata= run_calc_model_multi(jj,M,gfehs,gafes,fehs,afes,options,
vs,
potparams1,potparams2,potparams3,
distancefacs,
binned,alts,
False,None)
except TypeError:
continue
velps[cumulndata:cumulndata+tndata,:]= tvelps
velps2[cumulndata:cumulndata+tndata,:]= tvelps2
velps3[cumulndata:cumulndata+tndata,:]= tvelps3
data.extend(tdata)
zs.extend(tzs)
cumulndata+= tndata
bovy_plot.bovy_print()
bovy_plot.bovy_hist(data,bins=26,normed=True,color='k',
histtype='step',
xrange=xrange,xlabel=xlabel)
plotp= numpy.nansum(velps[:cumulndata,:],axis=0)/cumulndata
print numpy.sum(plotp)*(vs[1]-vs[0])
bovy_plot.bovy_plot(vs,plotp,'k-',overplot=True)
if alts:
plotp= numpy.nansum(velps2,axis=0)/cumulndata
bovy_plot.bovy_plot(vs,plotp,'k--',overplot=True)
plotp= numpy.nansum(velps3,axis=0)/cumulndata
bovy_plot.bovy_plot(vs,plotp,'k:',overplot=True)
if not left_legend is None:
bovy_plot.bovy_text(left_legend,top_left=True,size=_legendsize)
bovy_plot.bovy_text(r'$\mathrm{full\ subsample}$'
+'\n'+
'$%i \ \ \mathrm{stars}$' %
len(data),top_right=True,
size=_legendsize)
bovy_plot.bovy_end_print(args[0]+'model_data_g_'+options.group+'_'+options.type+'dist_all.'+options.ext)
if options.all: return None
#Plot zranges
#First determine the ranges that have nstars in them
rranges_nstars= 1000
zs= numpy.array(zs)
data= numpy.array(data)
tdata_z= sorted(numpy.fabs(zs))
nbins= numpy.ceil(len(tdata_z)/float(rranges_nstars))
rranges_nstars= int(numpy.floor(float(len(tdata_z))/nbins))
accum= rranges_nstars
zranges= [0.0]
while accum < len(tdata_z):
zranges.append(tdata_z[accum])
accum+= rranges_nstars
zranges.append(5.0)
print zranges
#zranges= [0.5,1.,1.5,2.,3.,4.]
nzranges= len(zranges)-1
sigzsd= numpy.empty(nzranges)
esigzsd= numpy.empty(nzranges)
sigzs1= numpy.empty(nzranges)
sigzs2= numpy.empty(nzranges)
sigzs3= numpy.empty(nzranges)
for ii in range(nzranges):
indx= (numpy.fabs(zs) >= zranges[ii])*(numpy.fabs(zs) < zranges[ii+1])
plotp= numpy.nansum(velps[indx,:],axis=0)/numpy.sum(indx)
yrange= [0.,1.35*numpy.nanmax(plotp)]
bovy_plot.bovy_print()
bovy_plot.bovy_hist(data[indx],bins=26,normed=True,color='k',
histtype='step',
yrange=yrange,
xrange=xrange,xlabel=xlabel)
sigzsd[ii]= numpy.std(data[indx][(numpy.fabs(data[indx]) < 100.)])
esigzsd[ii]= sigzsd[ii]/numpy.sqrt(float(len(data[indx][(numpy.fabs(data[indx]) < 100.)])))
sigzs1[ii]= numpy.sqrt(numpy.sum(vs**2.*plotp)/numpy.sum(plotp)-(numpy.sum(vs*plotp)/numpy.sum(plotp))**2.)
bovy_plot.bovy_plot(vs,plotp,'k-',overplot=True)
if alts:
plotp= numpy.nansum(velps2[indx,:],axis=0)/numpy.sum(indx)
sigzs2[ii]= numpy.sqrt(numpy.sum(vs**2.*plotp)/numpy.sum(plotp)-(numpy.sum(vs*plotp)/numpy.sum(plotp))**2.)
bovy_plot.bovy_plot(vs,plotp,'k--',overplot=True)
plotp= numpy.nansum(velps3[indx,:],axis=0)/numpy.sum(indx)
sigzs3[ii]= numpy.sqrt(numpy.sum(vs**2.*plotp)/numpy.sum(plotp)-(numpy.sum(vs*plotp)/numpy.sum(plotp))**2.)
bovy_plot.bovy_plot(vs,plotp,'k:',overplot=True)
bovy_plot.bovy_text(r'$ %i\ \mathrm{pc} \leq |Z| < %i\ \mathrm{pc}$' % (int(1000*zranges[ii]),int(1000*zranges[ii+1])),
# +'\n'+
# '$%i \ \ \mathrm{stars}$' % (numpy.sum(indx)),
top_right=True,
size=_legendsize)
bovy_plot.bovy_end_print(args[0]+'model_data_g_'+options.group+'_'+options.type+'dist_z%.1f_z%.1f.' % (zranges[ii],zranges[ii+1])+options.ext)
#Plot velocity dispersion as a function of |Z|
bovy_plot.bovy_print()
bovy_plot.bovy_plot((((numpy.roll(zranges,-1)+zranges)/2.)[:-1]),sigzsd,
'ko',
xlabel=r'$|Z|\ (\mathrm{kpc})$',
ylabel=r'$\sigma_z\ (\mathrm{km\,s}^{-1})$',
xrange=[0.,4.],
yrange=[0.,60.])
pyplot.errorbar(((numpy.roll(zranges,-1)+zranges)/2.)[:-1],sigzsd,
yerr=esigzsd,
marker='o',color='k',linestyle='none')
bovy_plot.bovy_plot((((numpy.roll(zranges,-1)+zranges)/2.)[:-1]),sigzs1,
'r+',overplot=True,ms=10.)
bovy_plot.bovy_plot((((numpy.roll(zranges,-1)+zranges)/2.)[:-1]),sigzs2,
'cx',overplot=True,ms=10.)
bovy_plot.bovy_plot((((numpy.roll(zranges,-1)+zranges)/2.)[:-1]),sigzs3,
'gd',overplot=True,ms=10.)
bovy_plot.bovy_end_print(args[0]+'model_data_g_'+options.group+'_'+options.type+'dist_szvsz.' +options.ext)
return None
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 plotVelComparisonDF(options,args):
#Read data etc.
print "Reading the data ..."
raw= read_rawdata(options)
#Bin the data
binned= pixelAfeFeh(raw,dfeh=options.dfeh,dafe=options.dafe)
#Map the bins with ndata > minndata in 1D
fehs, afes= [], []
for ii in range(len(binned.fehedges)-1):
for jj in range(len(binned.afeedges)-1):
data= binned(binned.feh(ii),binned.afe(jj))
if len(data) < options.minndata:
continue
#print binned.feh(ii), binned.afe(jj), len(data)
fehs.append(binned.feh(ii))
afes.append(binned.afe(jj))
nabundancebins= len(fehs)
fehs= numpy.array(fehs)
afes= numpy.array(afes)
if not options.singlefeh is None:
if True: #Just to keep indentation the same
#Set up single feh
indx= binned.callIndx(options.singlefeh,options.singleafe)
if numpy.sum(indx) == 0:
raise IOError("Bin corresponding to singlefeh and singleafe is empty ...")
allraw= copy.copy(raw)
raw= copy.copy(binned.data[indx])
#newerrstuff= []
#for ii in range(len(binned.data)):
# if indx[ii]: newerrstuff.append(errstuff[ii])
#errstuff= newerrstuff
print "Using %i data points ..." % (numpy.sum(indx))
#Bin again
binned= pixelAfeFeh(raw,dfeh=options.dfeh,dafe=options.dafe)
fehs, afes= [], []
for ii in range(len(binned.fehedges)-1):
for jj in range(len(binned.afeedges)-1):
data= binned(binned.feh(ii),binned.afe(jj))
if len(data) < options.minndata:
continue
fehs.append(binned.feh(ii))
afes.append(binned.afe(jj))
nabundancebins= len(fehs)
fehs= numpy.array(fehs)
afes= numpy.array(afes)
if options.singles:
run_abundance_singles_plotdens(options,args,fehs,afes)
return None
normintstuff= setup_normintstuff(options,raw,binned,fehs,afes,allraw)
##########POTENTIAL PARAMETERS####################
potparams1= numpy.array([numpy.log(2.6/8.),230./220.,numpy.log(400./8000.),0.266666666,0.])
potparams2= numpy.array([numpy.log(2.8/8.),230./220,numpy.log(400./8000.),0.266666666666,0.])
#potparams2= numpy.array([numpy.log(2.5/8.),1.,numpy.log(400./8000.),0.466666,0.,2.])
potparams3= numpy.array([numpy.log(2.6/8.),230./220.,
numpy.log(400./8000.),0.5333333,0.])
pop= 0 #assume first population
#Load savefile
if not options.init is None:
#Load initial parameters from file
savename= options.init
# spl= savename.split('.')
# newname= ''
# for ll in range(len(spl)-1):
# newname+= spl[ll]
# if not ll == len(spl)-2: newname+= '.'
# newname+= '_%i.' % pop
# newname+= spl[-1]
savefile= open(savename,'rb')
try:
if not _NOTDONEYET:
params= pickle.load(savefile)
mlogl= pickle.load(savefile)
logl= pickle.load(savefile)
except:
if savetopickle:
save_pickles(tmpfiles[jj],None)
return None
else:
return None
finally:
savefile.close()
else:
raise IOError("base filename not specified ...")
logl[numpy.isnan(logl)]= -numpy.finfo(numpy.dtype(numpy.float64)).max
#Set DF parameters as the maximum at R_d=2.4, f_h=0.4
#######DF PARAMETER RANGES###########
lnhr, lnsr, lnsz, rehr, resr, resz= approxFitResult(fehs[0],afes[0],
relerr=True)
#if rehr > 0.3: rehr= 0.3 #regularize
if True: rehr= 0.3 #regularize
#if resr > 0.3: resr= 0.3
#if resz > 0.3: resz= 0.3
if True: resr= 0.3
if True: resz= 0.3
hrs= numpy.linspace(-1.85714286,0.9,options.nhrs)
#hrs= numpy.linspace(lnhr-1.5*rehr,lnhr+1.5*rehr,options.nhrs)
if _VARYHSZ:
srs= numpy.linspace(numpy.log(0.5),numpy.log(2.),options.nsrs)#hsz now
else:
srs= numpy.linspace(lnsr-0.6*resz,lnsr+0.6*resz,options.nsrs)#USE ESZ
szs= numpy.linspace(lnsz-0.6*resz,lnsz+0.6*resz,options.nszs)
#hrs= numpy.linspace(lnhr-0.3,lnhr+0.3,options.nhrs)
#srs= numpy.linspace(lnsr-0.1,lnsr+0.1,options.nsrs)
#szs= numpy.linspace(lnsz-0.1,lnsz+0.1,options.nszs)
dvts= numpy.linspace(-0.35,0.05,options.ndvts)
#dvts= numpy.linspace(-0.05,0.05,options.ndvts)
pouts= numpy.linspace(10.**-5.,.5,options.npouts)
#indx= numpy.unravel_index(numpy.argmax(logl[3,0,0,3,:,:,:,:,:,0,0]),
indx= numpy.unravel_index(numpy.argmax(logl[3,0,0,4,:,:,:,0]),
logl[3,0,0,4,:,:,:,0].shape)
#tparams= numpy.array([dvts[indx[3]],hrs[indx[0]],
if _VARYHSZ:
tparams= numpy.array([0.,hrs[indx[0]],
#srs[indx[1]-2.*(indx[1] != 0)],
#szs[indx[2]-2.*(indx[2] != 0)],
lnsr,
szs[indx[2]],
numpy.log(8./_REFR0),
srs[indx[1]],
0.0,#logl[3,0,0,10,indx[0],indx[1],indx[2],2],#pouts[indx[4]],
0.,0.,0.,0.,0.])
else:
tparams= numpy.array([0.,hrs[indx[0]],
#srs[indx[1]-2.*(indx[1] != 0)],
#szs[indx[2]-2.*(indx[2] != 0)],
srs[indx[1]],
szs[indx[2]],
numpy.log(8./_REFR0),
numpy.log(7./_REFR0),0.,#pouts[indx[4]],
0.,0.,0.,0.,0.])
options.potential= 'dpdiskplhalofixbulgeflatwgasalt'
tparams= set_potparams(potparams1,tparams,options,1)
data= []
zs= []
velps= numpy.zeros((len(binned.data),options.nv))
velps[:,:]= numpy.nan
velps2= numpy.zeros((len(binned.data),options.nv))
velps2[:,:]= numpy.nan
velps3= numpy.zeros((len(binned.data),options.nv))
velps3[:,:]= numpy.nan
cumulndata= 0
if options.type.lower() == 'vz':
if options.group == 'aenhanced':
vs= numpy.linspace(-180.,180.,options.nv)
xrange=[-180.,180.]
bins= 39
else: # options.group == 'aenhanced':
vs= numpy.linspace(-120.,120.,options.nv)
xrange=[-120.,120.]
bins= 26
xlabel=r'$V_Z\ [\mathrm{km\,s}^{-1}]$'
elif options.type.lower() == 'vr':
if options.group == 'aenhanced':
vs= numpy.linspace(-220.,220.,options.nv)
xrange=[-220.,220.]
bins= 39
else: # options.group == 'aenhanced':
vs= numpy.linspace(-150.,150.,options.nv)
xrange=[-150.,150.]
bins= 26
xlabel=r'$V_R\ [\mathrm{km\,s}^{-1}]$'
elif options.type.lower() == 'vt':
if options.group == 'aenhanced':
vs= numpy.linspace(0.01,350.,options.nv)
xrange=[0.,350.]
bins= 39
else: # options.group == 'aenhanced':
vs= numpy.linspace(0.01,350.,options.nv)
xrange=[0.,350.]
bins= 39
xlabel=r'$V_T\ [\mathrm{km\,s}^{-1}]$'
thisdata= binned(fehs[pop],afes[pop])
velps[cumulndata:cumulndata+len(thisdata),:]= calc_model(tparams,options,thisdata,vs,normintstuff=normintstuff)
alts= True
if alts:
indx= numpy.unravel_index(numpy.argmax(logl[4,0,0,4,:,:,:,0]),
logl[4,0,0,4,:,:,:,0].shape)
#tparams= numpy.array([dvts[indx[3]],hrs[indx[0]],
if not _VARYHSZ:
tparams= numpy.array([0.,hrs[indx[0]],
#srs[indx[1]-1.*(indx[1] != 0)],
#szs[indx[2]-1.*(indx[2] != 0)],
srs[indx[1]],
szs[indx[2]],
numpy.log(8./_REFR0),
numpy.log(7./_REFR0),0.,#pouts[indx[4]],
0.,0.,0.,0.,0.,0.])
else:
tparams= numpy.array([0.,hrs[indx[0]],
#srs[indx[1]-1.*(indx[1] != 0)],
#szs[indx[2]-1.*(indx[2] != 0)],
lnsr,
szs[indx[2]],
numpy.log(8./_REFR0),
srs[indx[1]],0.,#pouts[indx[4]],
0.,0.,0.,0.,0.,0.])
#options.potential= 'dpdiskplhalodarkdiskfixbulgeflatwgasalt'
options.potential= 'dpdiskplhalofixbulgeflatwgasalt'
tparams= set_potparams(potparams2,tparams,options,1)
print "Working on model 2 ..."
velps2[cumulndata:cumulndata+len(thisdata),:]= calc_model(tparams,options,thisdata,vs)
indx= numpy.unravel_index(numpy.argmax(logl[3,0,0,8,:,:,:,0]),
logl[3,0,0,8,:,:,:,0].shape)
#tparams= numpy.array([dvts[indx[3]],hrs[indx[0]],
if _VARYHSZ:
tparams= numpy.array([0.,hrs[indx[0]],
#srs[indx[1]-2.*(indx[1] != 0)],
#szs[indx[2]-2.*(indx[2] != 0)],
lnsr,
szs[indx[2]],
numpy.log(8./_REFR0),
srs[indx[1]],0.,#pouts[indx[4]],
0.,0.,0.,0.,0.])
else:
tparams= numpy.array([0.,hrs[indx[0]],
#srs[indx[1]-2.*(indx[1] != 0)],
#szs[indx[2]-2.*(indx[2] != 0)],
srs[indx[1]],
szs[indx[2]],
numpy.log(8./_REFR0),
numpy.log(7./_REFR0),0.,#pouts[indx[4]],
0.,0.,0.,0.,0.])
options.potential= 'dpdiskplhalofixbulgeflatwgasalt'
tparams= set_potparams(potparams3,tparams,options,1)
print "Working on model 3 ..."
velps3[cumulndata:cumulndata+len(thisdata),:]= calc_model(tparams,options,thisdata,vs)
#Also add the correct data
vo= get_vo(tparams,options,1)
ro= get_ro(tparams,options)
if 'vr' in options.type.lower() or 'vt' in options.type.lower():
R= ((ro*_REFR0-thisdata.xc)**2.+thisdata.yc**2.)**(0.5)
cosphi= (_REFR0*ro-thisdata.xc)/R
sinphi= thisdata.yc/R
vR= -(thisdata.vxc-_VRSUN)*cosphi+(thisdata.vyc+_VTSUN)*sinphi
vT= (thisdata.vxc-_VRSUN)*sinphi+(thisdata.vyc+_VTSUN)*cosphi
if options.type.lower() == 'vz':
data.extend(thisdata.vzc+_VZSUN)
elif options.type.lower() == 'vr':
data.extend(vR)
elif options.type.lower() == 'vt':
data.extend(vT)
zs.extend(thisdata.zc)
cumulndata+= len(thisdata)
bovy_plot.bovy_print()
bovy_plot.bovy_hist(data,bins=26,normed=True,color='k',
histtype='step',
xrange=xrange,xlabel=xlabel)
plotp= numpy.nansum(velps,axis=0)/cumulndata
print numpy.sum(plotp)*(vs[1]-vs[0])
bovy_plot.bovy_plot(vs,plotp,'k-',overplot=True)
if alts:
plotp= numpy.nansum(velps2,axis=0)/cumulndata
bovy_plot.bovy_plot(vs,plotp,'k--',overplot=True)
plotp= numpy.nansum(velps3,axis=0)/cumulndata
bovy_plot.bovy_plot(vs,plotp,'k:',overplot=True)
bovy_plot.bovy_text(r'$\mathrm{full\ subsample}$'
+'\n'+
'$%i \ \ \mathrm{stars}$' %
len(data),top_right=True,
size=_legendsize)
bovy_plot.bovy_end_print(args[0]+'model_data_g_'+options.type+'dist_all.'+options.ext)
if options.all: return None
#Plot zranges
zranges= numpy.array([0.5,1.,1.5,2.,3.,4.])
nzranges= len(zranges)-1
zs= numpy.array(zs)
data= numpy.array(data)
sigzsd= numpy.empty(nzranges)
esigzsd= numpy.empty(nzranges)
sigzs1= numpy.empty(nzranges)
sigzs2= numpy.empty(nzranges)
sigzs3= numpy.empty(nzranges)
for ii in range(nzranges):
indx= (numpy.fabs(zs) >= zranges[ii])*(numpy.fabs(zs) < zranges[ii+1])
bovy_plot.bovy_print()
bovy_plot.bovy_hist(data[indx],bins=26,normed=True,color='k',
histtype='step',
xrange=xrange,xlabel=xlabel)
sigzsd[ii]= numpy.std(data[indx][(numpy.fabs(data[indx]) < 100.)])
esigzsd[ii]= sigzsd[ii]/numpy.sqrt(float(len(data[indx][(numpy.fabs(data[indx]) < 100.)])))
plotp= numpy.nansum(velps[indx,:],axis=0)/numpy.sum(indx)
sigzs1[ii]= numpy.sqrt(numpy.sum(vs**2.*plotp)/numpy.sum(plotp)-(numpy.sum(vs*plotp)/numpy.sum(plotp))**2.)
bovy_plot.bovy_plot(vs,plotp,'k-',overplot=True)
if alts:
plotp= numpy.nansum(velps2[indx,:],axis=0)/numpy.sum(indx)
sigzs2[ii]= numpy.sqrt(numpy.sum(vs**2.*plotp)/numpy.sum(plotp)-(numpy.sum(vs*plotp)/numpy.sum(plotp))**2.)
bovy_plot.bovy_plot(vs,plotp,'k--',overplot=True)
plotp= numpy.nansum(velps3[indx,:],axis=0)/numpy.sum(indx)
sigzs3[ii]= numpy.sqrt(numpy.sum(vs**2.*plotp)/numpy.sum(plotp)-(numpy.sum(vs*plotp)/numpy.sum(plotp))**2.)
bovy_plot.bovy_plot(vs,plotp,'k:',overplot=True)
bovy_plot.bovy_text(r'$ %i\ \mathrm{pc} \leq |Z| < %i\ \mathrm{pc}$' % (int(1000*zranges[ii]),int(1000*zranges[ii+1]))
+'\n'+
'$%i \ \ \mathrm{stars}$' %
(numpy.sum(indx)),top_right=True,
size=_legendsize)
bovy_plot.bovy_end_print(args[0]+'model_data_g_'+options.type+'dist_z%.1f_z%.1f.' % (zranges[ii],zranges[ii+1])+options.ext)
#Plot velocity dispersion as a function of |Z|
bovy_plot.bovy_print()
bovy_plot.bovy_plot((((numpy.roll(zranges,-1)+zranges)/2.)[:5]),sigzsd,
'ko',
xlabel=r'$|Z|\ (\mathrm{kpc})$',
ylabel=r'$\sigma_z\ (\mathrm{km\,s}^{-1})$',
xrange=[0.,4.],
yrange=[0.,60.])
pyplot.errorbar(((numpy.roll(zranges,-1)+zranges)/2.)[:5],sigzsd,
yerr=esigzsd,
marker='o',color='k',linestyle='none')
bovy_plot.bovy_plot((((numpy.roll(zranges,-1)+zranges)/2.)[:5]),sigzs1,
'r+',overplot=True,ms=10.)
bovy_plot.bovy_plot((((numpy.roll(zranges,-1)+zranges)/2.)[:5]),sigzs2,
'cx',overplot=True,ms=10.)
bovy_plot.bovy_plot((((numpy.roll(zranges,-1)+zranges)/2.)[:5]),sigzs3,
'gd',overplot=True,ms=10.)
bovy_plot.bovy_end_print(args[0]+'model_data_g_'+options.type+'dist_szvsz.'+options.ext)
return None
else:
for ii in range(options.nvlos):
print ii
pvlosalljhk[ii] = pvlosplate(params, vlos[ii],
alljhkdata, df, options,
logpiso, logpisodwarf,
iso)
pvlosalljhk -= logsumexp(pvlosalljhk)
pvlosalljhk = numpy.exp(pvlosalljhk)
#Plot data
bovy_plot.bovy_print()
hist, xvec, p = bovy_plot.bovy_hist(
thesedata['VHELIO'],
range=[-200., 200.],
bins=31,
histtype='step',
color='k',
xlabel=
r'$\mathrm{heliocentric}\ V_{\mathrm{los}}\ [\mathrm{km\ s}^{-1}]$'
)
#Normalize prediction
data_int = numpy.sum(hist) * (xvec[1] - xvec[0])
pvlos *= data_int / numpy.sum(pvlos) / (vlos[1] - vlos[0])
bovy_plot.bovy_plot(vlos,
pvlos,
'-',
color='0.6',
overplot=True,
lw=3.)
if _PLOTZERO:
pvloszero *= data_int / numpy.sum(pvloszero) / (vlos[1] -
#Now plot the selection function in real-space
bovy_plot.bovy_print(fig_width=6.)
apo.plot_selfunc_xy(vmax=15.)
bovy_plot.bovy_end_print(basefilename+'sfxy.%s' % _EXT)
bovy_plot.bovy_print(fig_width=6.)
apo.plot_selfunc_xy(type='rz',vmax=15.)
bovy_plot.bovy_end_print(basefilename+'sfrz.%s' % _EXT)
#Now calculate the KS probabilities and plot the histogram
ksshort= apo.check_consistency('short','short')
ksmedium= apo.check_consistency('medium','medium')
kslong= apo.check_consistency('long','long')
xrange= [0.,1.]
bovy_plot.bovy_print()
bovy_plot.bovy_hist(ksshort,range=xrange,bins=21,
xlabel=r'$\mathrm{KS\ probability}$',#\ that\ the\ spectroscopic\ sample}$'+'\n'+r'$\mathrm{was\ drawn\ from\ the\ photometric\ sample}$'+'\n'+r'$\times\ \mathrm{the\ model\ selection\ function}$',
ylabel=r'$\mathrm{distribution}$',
yrange=[0.,4.],
ec='k',histtype='step',normed=True)
bovy_plot.bovy_hist(ksmedium,range=xrange,normed=True,
bins=11,overplot=True,
ec='k',histtype='step',
ls='dashed')
bovy_plot.bovy_hist(kslong,range=xrange,normed=True,
bins=11,overplot=True,
ec='k',histtype='step',
ls='dashdot')
#Add proxies for legend
from matplotlib.lines import Line2D
lineshort= Line2D([0.],[0.],linestyle='-',color='k')
linemedium= Line2D([0.],[0.],linestyle='--',color='k')
linelong= Line2D([0.],[0.],linestyle='-.',color='k')
def main(args: Optional[list] = None,
opts: Optional[argparse.Namespace] = None):
"""Fit Combination Pal5 and GD1 to MW Potential Script Function.
Parameters
----------
args : list, optional
an optional single argument that holds the sys.argv list,
except for the script name (e.g., argv[1:])
"""
if opts is not None and args is None:
pass
else:
parser = make_parser()
opts = parser.parse_args(args)
fpath = opts.fpath + "/" if not opts.fpath.endswith("/") else opts.fpath
opath = opts.opath + "/" if not opts.opath.endswith("/") else opts.opath
# -----------------------
# Adding in the force measurements from Pal 5 *and* GD-1; also fitting
# $R_0$ and $V_c(R_0)$
plt.figure(figsize=(16, 5))
p_b15_pal5gd1_voro = mw_pot.fit(
fitc=True,
c=None,
addpal5=True,
addgd1=True,
fitvoro=True,
mc16=True,
plots=fpath + "fit.pdf",
)
# -----------------------
samples_savefilename = opath + "mwpot14varyc-fitvoro-pal5gd1-samples.pkl"
if os.path.exists(samples_savefilename):
with open(samples_savefilename, "rb") as savefile:
s = pickle.load(savefile)
else:
s = mw_pot.sample(
nsamples=100000,
params=p_b15_pal5gd1_voro[0],
fitc=True,
c=None,
plots=fpath + "mwpot14varyc-fitvoro-pal5gd1-samples.pdf",
mc16=True,
addpal5=True,
addgd1=True,
fitvoro=True,
)
save_pickles(samples_savefilename, s)
# -----------------------
plt.figure()
mw_pot.plot_samples(
s,
True,
True,
addpal5=True,
addgd1=True,
savefig=fpath + "mwpot14varyc-fitvoro-pal5gd1-samples-corner.pdf",
)
# -----------------------
bf_savefilename = opath + "mwpot14varyc-bf.pkl" # should already exist
if os.path.exists(bf_savefilename):
with open(bf_savefilename, "rb") as savefile:
cs = pickle.load(savefile)
bf_params = pickle.load(savefile)
else:
cs = np.arange(0.5, 4.1, 0.1)
bf_params = []
for c in tqdm(cs):
dum = mw_pot.fit(
fitc=False,
c=c,
plots=fpath + "mwpot14varyc-bf-fit.pdf",
)
bf_params.append(dum[0])
save_pickles(bf_savefilename, cs, bf_params)
# -----------------------
plt.figure()
bovy_plot.bovy_print(
axes_labelsize=17.0,
text_fontsize=12.0,
xtick_labelsize=15.0,
ytick_labelsize=15.0,
)
su.plot_mcmc_c(
s,
True,
cs,
bf_params,
savefig=fpath + "mwpot14varyc-bf-combo_pal5_gd1-dependence.pdf",
)
if save_figures:
plt.savefig(
os.path.join(
os.getenv("PAPERSDIR"),
"2016-mwhalo-shape",
"mwpot14-varyc-wp5g1.pdf",
),
bbox_inches="tight",
)
# -----------------------
plt.figure(figsize=(4, 4))
cindx = 9
dum = bovy_plot.bovy_hist(
s[cindx],
bins=36,
histtype="step",
lw=2.0,
xlabel=r"$c/a$",
xrange=[0.5, 1.5],
normed=True,
)
plt.savefig(fpath + "mwpot14varyc-bf-combo_pal5_gd1-shape_hist.pdf")
with open(opath + "fit_potential_combo-pal5-gd1.txt", "w") as file:
sortedc = np.array(sorted(s[cindx][-50000:]))
file.write("2.5%% and 0.5%% lower limits: %.3f, %.3f" % (
sortedc[int(np.floor(0.025 * len(sortedc)))],
sortedc[int(np.floor(0.005 * len(sortedc)))],
))
file.write("2.5%% and 0.5%% upper limits: %.3f, %.3f" % (
sortedc[int(np.floor(0.975 * len(sortedc)))],
sortedc[int(np.floor(0.995 * len(sortedc)))],
))
file.write("Median, 68%% confidence: %.3f, %.3f, %.3f" % (
np.median(sortedc),
sortedc[int(np.floor(0.16 * len(sortedc)))],
sortedc[int(np.floor(0.84 * len(sortedc)))],
))
file.write("Mean, std. dev.: %.2f,%.2f" % (
np.mean(sortedc),
np.std(sortedc),
))
# -----------------------
# What is the constraint on the mass of the halo?
tR = 20.0 / REFR0
skip = 1
hmass = []
for sa in tqdm(s.T[::skip]):
pot = mw_pot.setup_potential(
sa,
sa[-1],
True,
False,
REFR0 * sa[8],
REFV0 * sa[7],
fitvoro=True,
)
hmass.append(-integrate.quad(
lambda x: tR**2.0 * potential.evaluaterforces(
pot[2], tR * x, tR * np.sqrt(1.0 - x**2.0), phi=0.0),
0.0,
1.0,
)[0] * bovy_conversion.mass_in_1010msol(REFV0, REFR0) / 10.0)
hmass = np.array(hmass)
with open(opath + "fit_potential_combo-pal5-gd1.txt",
"a") as file: # append
file.write("\nMass Constraints:")
sortedhm = np.array(sorted(hmass))
file.write("2.5%% and 0.5%% lower limits: %.2f, %.2f" % (
sortedhm[int(np.floor(0.025 * len(sortedhm)))],
sortedhm[int(np.floor(0.005 * len(sortedhm)))],
))
file.write("2.5%% and 0.5%% upper limits: %.2f, %.2f" % (
sortedhm[int(np.floor(0.975 * len(sortedhm)))],
sortedhm[int(np.floor(0.995 * len(sortedhm)))],
))
file.write("Median, 68%% confidence: %.2f, %.2f, %.2f" % (
np.median(sortedhm),
sortedhm[int(np.floor(0.16 * len(sortedhm)))],
sortedhm[int(np.floor(0.84 * len(sortedhm)))],
))
# -----------------------
bovy_plot.scatterplot(
hmass,
s[-1, ::skip],
"k,",
onedhists=True,
bins=31,
xrange=[0.5, 1.5],
yrange=[0.5, 1.5],
xlabel=r"$M_{\mathrm{halo}} (r<20\,\mathrm{kpc})\,(M_\odot)$",
ylabel=r"$c/a$",
)
plt.savefig(fpath + "scatterplot.pdf")
def plotCompareData(sample,savename,plotname):
locs= ['highb','outdisk','meddisk','indisk']
indices= [highbIndx,outDiskIndx,betwDiskIndx,inDiskIndx]
data_indices= [data_highbIndx,data_outDiskIndx,
data_betwDiskIndx,data_inDiskIndx]
# Full prediction for numbers
Xs,pd= compareDataModel.predict_spacedist(bf_brexp,
numpy.array(locations),
copy.deepcopy(effsel_mar),
distmods,
type='tribrokenexpflare',coord='dm')
for loc, index, data_index in zip(locs,indices,data_indices):
bovy_plot.bovy_print(axes_labelsize=20,text_fontsize=20,
xtick_labelsize=20,ytick_labelsize=20)
# High |b|
# Marshall is fiducial
Xs,pdt= compareDataModel.predict_spacedist(bf_brexp,
numpy.array(locations)[index],
copy.deepcopy(effsel_mar)[index],
distmods,type='tribrokenexpflare',
coord='dm')
if (sample.lower() == 'solar' and \
(loc.lower() == 'outdisk' or loc.lower() == 'highb')) \
or (sample.lower() == 'highfeh' and loc.lower() == 'highb'):
yrange=[0.,
1.6*numpy.amax(pdt/numpy.sum(pdt)/(Xs[1]-Xs[0]))]
else:
yrange=[0.,
1.4*numpy.amax(pdt/numpy.sum(pdt)/(Xs[1]-Xs[0]))]
bovy_plot.bovy_hist(ldata['RC_DM_H'][data_index],
histtype='stepfilled',
normed=True,
lw=_LW,
range=[7.,15.5],
bins=round(numpy.sqrt(numpy.sum(data_index))*2.),
yrange=yrange,
ec='k',fc='0.75',
xlabel=r'$\mu$')
line_mar= bovy_plot.bovy_plot(Xs,pdt/numpy.sum(pdt)/(Xs[1]-Xs[0]),
color='r',
lw=2.*_LW,overplot=True,zorder=12)
bovy_plot.bovy_text(r'$%i\%% = %i / %i\ \mathrm{stars}$' \
% (int(round(float(numpy.sum(data_index))/len(ldata)*100.)),
numpy.sum(data_index),
len(ldata))
+'\n'+r'$%i\%% = %i / %i\ \mathrm{predicted}$' \
% (int(round(numpy.sum(pdt)/numpy.sum(pd)*100.)),
numpy.sum(pdt)/numpy.sum(pd)*len(ldata),len(ldata)),
top_left=True,size=16.)
# Green
Xs,pdt= compareDataModel.predict_spacedist(bf_brexp_g15,
numpy.array(locations)[index],
copy.deepcopy(effsel)[index],
distmods,type='tribrokenexpflare',
coord='dm')
line_g15= bovy_plot.bovy_plot(Xs,pdt/numpy.sum(pdt)/(Xs[1]-Xs[0]),
color='b',
lw=_LW,overplot=True,zorder=13)
# Drimmel
Xs,pdt= compareDataModel.predict_spacedist(bf_brexp_drim,
numpy.array(locations)[index],
copy.deepcopy(effsel_drim)[index],
distmods,type='tribrokenexpflare',
coord='dm')
line_drim= bovy_plot.bovy_plot(Xs,pdt/numpy.sum(pdt)/(Xs[1]-Xs[0]),
color='gold',
lw=_LW,overplot=True,zorder=12)
# Sale
Xs,pdt= compareDataModel.predict_spacedist(bf_brexp_sale,
numpy.array(locations)[index],
copy.deepcopy(effsel_sale)[index],
distmods,type='tribrokenexpflare',
coord='dm')
line_sale= bovy_plot.bovy_plot(Xs,pdt/numpy.sum(pdt)/(Xs[1]-Xs[0]),
color='c',
lw=_LW,overplot=True,zorder=12)
# Zero
Xs,pdt= compareDataModel.predict_spacedist(bf_brexp_zero,
numpy.array(locations)[index],
copy.deepcopy(effsel_zero)[index],
distmods,type='tribrokenexpflare',
coord='dm')
line_zero= bovy_plot.bovy_plot(Xs,pdt/numpy.sum(pdt)/(Xs[1]-Xs[0]),
color='k',
ls='-',lw=_LW,overplot=True,zorder=10)
# Marshall + exp
Xs,pdt= compareDataModel.predict_spacedist(bf_exp,
numpy.array(locations)[index],
copy.deepcopy(effsel_mar)[index],
distmods,type='expplusconst',
coord='dm')
line_exp= bovy_plot.bovy_plot(Xs,pdt/numpy.sum(pdt)/(Xs[1]-Xs[0]),
color='r',
lw=2*_LW,overplot=True,zorder=10,ls=':')
# Marshall + twoexp
Xs,pdt= compareDataModel.predict_spacedist(bf_twoexp,
numpy.array(locations)[index],
copy.deepcopy(effsel_mar)[index],
distmods,type='tribrokentwoexp',
coord='dm')
line_twoexp= bovy_plot.bovy_plot(Xs,pdt/numpy.sum(pdt)/(Xs[1]-Xs[0]),
color='r',
lw=2*_LW,overplot=True,zorder=11,
ls='--')
if sample.lower() == 'lowlow' or sample.lower() == 'highalpha':
if loc.lower() == 'highb':
pyplot.annotate(r'$|b| > 10^\circ$',
(0.5,1.085),xycoords='axes fraction',
horizontalalignment='center',
verticalalignment='top',
size=20.)
elif loc.lower() == 'indisk':
pyplot.annotate(r'$l < 70^\circ, |b| \leq 10^\circ$',
(0.5,1.085),xycoords='axes fraction',
horizontalalignment='center',
verticalalignment='top',
size=20.)
elif loc.lower() == 'meddisk':
pyplot.annotate(r'$70^\circ \leq l \leq 140^\circ, |b| \leq 10^\circ$',
(0.5,1.085),xycoords='axes fraction',
horizontalalignment='center',
verticalalignment='top',
size=20.)
elif loc.lower() == 'outdisk':
pyplot.annotate(r'$140^\circ < l < 250^\circ, |b| \leq 10^\circ$',
(0.5,1.085),xycoords='axes fraction',
horizontalalignment='center',
verticalalignment='top',
size=20.)
# Legend
if loc.lower() == 'meddisk':
pyplot.legend((line_mar[0],line_exp[0],line_twoexp[0]),
(r'$\mathrm{Marshall\ et\ al.\ (2006)}$'
+'\n'+r'$\mathrm{broken\ exp.\ w/\ flare}$',
r'$\mathrm{single\ exp.}$',
r'$\mathrm{broken\ exp.\ w/\ 2}\ h_Z$'),
loc='lower right',bbox_to_anchor=(.66,.42),
numpoints=8,
prop={'size':14},
frameon=False)
elif loc.lower() == 'outdisk':
pyplot.legend((line_g15[0],line_sale[0],line_drim[0],
line_zero[0]),
(r'$\mathrm{Green\ et\ al.\ (2015)}$',
r'$\mathrm{Sale\ et\ al.\ (2014)}$',
r'$\mathrm{Drimmel\ et\ al.\ (2003)}$',
r'$\mathrm{zero\ extinction}$'),
loc='lower right',bbox_to_anchor=(.66,.42),
numpoints=8,
prop={'size':14},
frameon=False)
if loc.lower() == 'highb':
if sample.lower() == 'lowlow':
bovy_plot.bovy_text(r'$\mathrm{low\ [Fe/H]}$',
top_right=True,size=18.)
elif sample.lower() == 'solar':
bovy_plot.bovy_text(r'$\mathrm{solar}$',
top_right=True,size=18.)
elif sample.lower() == 'highfeh':
bovy_plot.bovy_text(r'$\mathrm{high\ [Fe/H]}$',
top_right=True,size=18.)
elif sample.lower() == 'highalpha':
bovy_plot.bovy_text(r'$\mathrm{high}\ [\alpha/\mathrm{Fe]}$',
top_right=True,size=18.)
bovy_plot.bovy_end_print(plotname.replace('LOC',loc))
return None
def plotVelComparisonDFMulti(options,args):
#Read data etc.
print "Reading the data ..."
raw= read_rawdata(options)
#Bin the data
binned= pixelAfeFeh(raw,dfeh=options.dfeh,dafe=options.dafe)
#Map the bins with ndata > minndata in 1D
fehs, afes= [], []
for ii in range(len(binned.fehedges)-1):
for jj in range(len(binned.afeedges)-1):
data= binned(binned.feh(ii),binned.afe(jj))
if len(data) < options.minndata:
continue
#print binned.feh(ii), binned.afe(jj), len(data)
fehs.append(binned.feh(ii))
afes.append(binned.afe(jj))
nabundancebins= len(fehs)
fehs= numpy.array(fehs)
afes= numpy.array(afes)
gafes, gfehs, left_legend= getMultiComparisonBins(options)
if options.usemedianpotential:
potparams= get_median_potential(options,nabundancebins)
print "Median potential parameters: ", potparams
M= len(gfehs)
#Check whether fits exist, if not, pop
removeBins= numpy.ones(M,dtype='bool')
for jj in range(M):
#Find pop corresponding to this bin
pop= numpy.argmin((gfehs[jj]-fehs)**2./0.1+(gafes[jj]-afes)**2./0.0025)
#Load savefile
if not options.init is None:
#Load initial parameters from file
savename= options.init
spl= savename.split('.')
newname= ''
for ll in range(len(spl)-1):
newname+= spl[ll]
if not ll == len(spl)-2: newname+= '.'
newname+= '_%i.' % pop
newname+= spl[-1]
if not os.path.exists(newname):
removeBins[jj]= False
else:
raise IOError("base filename not specified ...")
if numpy.sum(removeBins) == 0:
raise IOError("None of the group bins have been fit ...")
elif numpy.sum(removeBins) < M:
#Some bins have not been fit yet, and have to be remove
gfehs= list((numpy.array(gfehs))[removeBins])
gafes= list((numpy.array(gafes))[removeBins])
print "Only using %i bins out of %i ..." % (numpy.sum(removeBins),M)
M= len(gfehs)
data= []
zs= []
velps= numpy.zeros((len(binned.data),options.nv))
velps[:,:]= numpy.nan
velps2= numpy.zeros((len(binned.data),options.nv))
velps2[:,:]= numpy.nan
velps3= numpy.zeros((len(binned.data),options.nv))
velps3[:,:]= numpy.nan
cumulndata= 0
if options.type.lower() == 'vz':
if options.group == 'aenhanced':
vs= numpy.linspace(-180.,180.,options.nv)
xrange=[-180.,180.]
bins= 39
else: # options.group == 'aenhanced':
vs= numpy.linspace(-120.,120.,options.nv)
xrange=[-120.,120.]
bins= 26
xlabel=r'$V_Z\ [\mathrm{km\,s}^{-1}]$'
elif options.type.lower() == 'vr':
if options.group == 'aenhanced':
vs= numpy.linspace(-220.,220.,options.nv)
xrange=[-220.,220.]
bins= 39
else: # options.group == 'aenhanced':
vs= numpy.linspace(-150.,150.,options.nv)
xrange=[-150.,150.]
bins= 26
xlabel=r'$V_R\ [\mathrm{km\,s}^{-1}]$'
elif options.type.lower() == 'vt':
if options.group == 'aenhanced':
vs= numpy.linspace(0.01,350.,options.nv)
xrange=[0.,350.]
bins= 39
else: # options.group == 'aenhanced':
vs= numpy.linspace(0.01,350.,options.nv)
xrange=[0.,350.]
bins= 39
xlabel=r'$V_T\ [\mathrm{km\,s}^{-1}]$'
for jj in range(M):
print "Working on group %i / %i ..." % (jj+1,M)
#Find pop corresponding to this bin
pop= numpy.argmin((gfehs[jj]-fehs)**2./0.1+(gafes[jj]-afes)**2./0.0025)
#Load savefile
if not options.init is None:
#Load initial parameters from file
savename= options.init
spl= savename.split('.')
newname= ''
for ll in range(len(spl)-1):
newname+= spl[ll]
if not ll == len(spl)-2: newname+= '.'
newname+= '_%i.' % pop
newname+= spl[-1]
savefile= open(newname,'rb')
tparams= pickle.load(savefile)
savefile.close()
else:
raise IOError("base filename not specified ...")
if options.usemedianpotential:
tparams= set_potparams(potparams,tparams,options,1)
print tparams
thisdata= binned(fehs[pop],afes[pop])
velps[cumulndata:cumulndata+len(thisdata),:]= calc_model(tparams,options,thisdata,vs)
alts= True
if alts:
if not options.usemedianpotential:
potparams= get_potparams(tparams,options,1)
if options.potential.lower() == 'flatlog':
tparams= set_potparams([potparams[0]*1.05,potparams[1]],
tparams,options,1)
velps2[cumulndata:cumulndata+len(thisdata),:]= calc_model(tparams,options,thisdata,vs)
tparams= set_potparams([potparams[0]*0.95,potparams[1]],
tparams,options,1)
velps3[cumulndata:cumulndata+len(thisdata),:]= calc_model(tparams,options,thisdata,vs)
#Also add the correct data
vo= get_vo(tparams,options,1)
ro= get_ro(tparams,options)
if 'vr' in options.type.lower() or 'vt' in options.type.lower():
R= ((ro*_REFR0-thisdata.xc)**2.+thisdata.yc**2.)**(0.5)
cosphi= (_REFR0*ro-thisdata.xc)/R
sinphi= thisdata.yc/R
vR= -(thisdata.vxc-_VRSUN)*cosphi+(thisdata.vyc+_VTSUN)*sinphi
vT= (thisdata.vxc-_VRSUN)*sinphi+(thisdata.vyc+_VTSUN)*cosphi
if options.type.lower() == 'vz':
data.extend(thisdata.vzc+_VZSUN)
elif options.type.lower() == 'vr':
data.extend(vR)
elif options.type.lower() == 'vt':
data.extend(vT)
zs.extend(thisdata.zc)
cumulndata+= len(thisdata)
bovy_plot.bovy_print()
bovy_plot.bovy_hist(data,bins=26,normed=True,color='k',
histtype='step',
xrange=xrange,xlabel=xlabel)
plotp= numpy.nansum(velps,axis=0)/cumulndata
print numpy.sum(plotp)*(vs[1]-vs[0])
bovy_plot.bovy_plot(vs,plotp,'k-',overplot=True)
if alts:
plotp= numpy.nansum(velps2,axis=0)/cumulndata
bovy_plot.bovy_plot(vs,plotp,'k--',overplot=True)
plotp= numpy.nansum(velps3,axis=0)/cumulndata
bovy_plot.bovy_plot(vs,plotp,'k:',overplot=True)
if not left_legend is None:
bovy_plot.bovy_text(left_legend,top_left=True,size=_legendsize)
bovy_plot.bovy_text(r'$\mathrm{full\ subsample}$'
+'\n'+
'$%i \ \ \mathrm{stars}$' %
len(data),top_right=True,
size=_legendsize)
bovy_plot.bovy_end_print(args[0]+'model_data_g_'+options.group+'_'+options.type+'dist_all.'+options.ext)
if options.all: return None
#Plot zranges
zranges= [0.5,1.,1.5,2.,3.,4.]
nzranges= len(zranges)-1
zs= numpy.array(zs)
data= numpy.array(data)
for ii in range(nzranges):
indx= (numpy.fabs(zs) >= zranges[ii])*(numpy.fabs(zs) < zranges[ii+1])
bovy_plot.bovy_print()
bovy_plot.bovy_hist(data[indx],bins=26,normed=True,color='k',
histtype='step',
xrange=xrange,xlabel=xlabel)
plotp= numpy.nansum(velps[indx,:],axis=0)/numpy.sum(indx)
bovy_plot.bovy_plot(vs,plotp,'k-',overplot=True)
if alts:
plotp= numpy.nansum(velps2[indx,:],axis=0)/numpy.sum(indx)
bovy_plot.bovy_plot(vs,plotp,'k--',overplot=True)
plotp= numpy.nansum(velps3[indx,:],axis=0)/numpy.sum(indx)
bovy_plot.bovy_plot(vs,plotp,'k:',overplot=True)
bovy_plot.bovy_text(r'$ %i\ \mathrm{pc} \leq |Z| < %i\ \mathrm{pc}$' % (int(1000*zranges[ii]),int(1000*zranges[ii+1]))
+'\n'+
'$%i \ \ \mathrm{stars}$' %
(numpy.sum(indx)),top_right=True,
size=_legendsize)
bovy_plot.bovy_end_print(args[0]+'model_data_g_'+options.group+'_'+options.type+'dist_z%.1f_z%.1f.' % (zranges[ii],zranges[ii+1])+options.ext)
return None
def verysimplenfwfit(plotfilename,wcprior=False,wmassprior=False):
#Fit
p= optimize.fmin_powell(chi2,[-0.5,0.7],args=(wcprior,wmassprior))
print mvir(p), conc(p)*numpy.exp(p[1])*8.
vo= numpy.exp(p[0])
a= numpy.exp(p[1])
nfw= potential.NFWPotential(normalize=1.,a=a)
rs= numpy.linspace(0.01,350.,1001)
masses= numpy.array([nfw.mass(r/8.) for r in rs])
bovy_plot.bovy_print(fig_width=6.)
bovy_plot.bovy_plot(rs,
masses*bovy_conversion.mass_in_1010msol(220.*vo,8.)/100.,
'k-',loglog=True,
xlabel=r'$R\,(\mathrm{kpc})$',
ylabel=r'$M(<R)\,(10^{12}\,M_\odot)$',
yrange=[0.01,1.2],
xrange=[3.,400.])
if _USE_ALL_XUE:
plotx= [10.]
plotx.extend(list(_XUER))
ploty= [4.5/100.]
ploty.extend(list(_XUEMASS/100.))
plotyerr= [1.5/100]
plotyerr.extend(list(_XUEMASS_ERR/100.))
pyplot.errorbar(plotx,ploty,yerr=plotyerr,marker='o',ls='none',
color='k')
else:
pyplot.errorbar([10.,60.],[4.5/100.,3.5/10.],yerr=[1.5/100.,0.7/10.],
marker='o',ls='none',color='k')
pyplot.errorbar([150.],[7.5/10.],[2.5/10.],marker='None',color='k')
dx= .5
arr= FancyArrowPatch(posA=(7.,4./100.),posB=(numpy.exp(numpy.log(7.)+dx),numpy.exp(numpy.log(4./100.)+1.5*dx)),arrowstyle='->',connectionstyle='arc3,rad=%4.2f' % (0.),shrinkA=2.0, shrinkB=2.0,mutation_scale=20.0, mutation_aspect=None,fc='k')
ax = pyplot.gca()
ax.add_patch(arr)
#Sample MCMC to get virial mass uncertainty
samples= bovy_mcmc.markovpy(p,
0.05,
lnlike,
(wcprior,wmassprior),
isDomainFinite=[[False,False],[False,False]],
domain=[[0.,0.],[0.,0.]],
nsamples=10000,
nwalkers=6)
mvirs= numpy.array([mvir(x) for x in samples])
concs= numpy.array([conc(x) for x in samples])
indx= True-numpy.isnan(mvirs)
mvirs= mvirs[indx]
indx= True-numpy.isnan(concs)
concs= concs[indx]
rvirs= concs[indx]*numpy.array([numpy.exp(x[1]) for x in samples])*8.
bovy_plot.bovy_text(r'$M_{\mathrm{vir}} = %.2f\pm%.2f\times10^{12}\,M_\odot$' % (numpy.median(mvirs),1.4826*numpy.median(numpy.fabs(mvirs-numpy.median(mvirs)))) +'\n'+
r'$r_{\mathrm{vir}} = %i\pm%i\,\mathrm{kpc}$' % (numpy.median(rvirs),1.4826*numpy.median(numpy.fabs(rvirs-numpy.median(rvirs))))+'\n'+
r'$c = %.1f\pm%.1f$' % (numpy.median(concs),1.4826*numpy.median(numpy.fabs(concs-numpy.median(concs)))),
top_left=True,size=18.)
#Create inset with PDF
insetAxes= pyplot.axes([0.55,0.22,0.3,0.3])
pyplot.sca(insetAxes)
bovy_plot.bovy_hist(mvirs,range=[0.,3.],bins=51,histtype='step',
color='k',
normed=True,
overplot=True)
insetAxes.set_xlim(0.,3.)
insetAxes.set_ylim(0.,1.49)
insetAxes.set_xlabel(r'$M_{\mathrm{vir}}\,(10^{12}\,M_\odot)$')
bovy_plot._add_ticks()
bovy_plot.bovy_end_print(plotfilename)
def calc_es():
savefilename= 'myes.sav'
if os.path.exists(savefilename):
savefile= open(savefilename,'rb')
mye= pickle.load(savefile)
e= pickle.load(savefile)
savefile.close()
else:
#Read data
dialect= csv.excel
dialect.skipinitialspace=True
reader= csv.reader(open('../data/Dierickx-etal-tab2.txt','r'),delimiter=' ',dialect=dialect)
vxvs= []
es= []
vphis= []
vxs= []
vys= []
vzs= []
ls= []
for row in reader:
thisra= float(row[3])
thisdec= float(row[4])
thisd= float(row[17])/1000.
thispmra= float(row[13])
thispmdec= float(row[15])
thisvlos= float(row[11])
thise= float(row[26])
vxvs.append([thisra,thisdec,thisd,thispmra,thispmdec,thisvlos])
es.append(thise)
vphis.append(float(row[25]))
vxs.append(float(row[19]))
vys.append(float(row[21]))
vzs.append(float(row[23]))
ls.append(float(row[5]))
vxvv= nu.array(vxvs)
e= nu.array(es)
vphi= nu.array(vphis)
vx= nu.array(vxs)
vy= nu.array(vys)
vz= nu.array(vzs)
l= nu.array(ls)
#Define potential
lp= LogarithmicHaloPotential(normalize=1.)
mp= MiyamotoNagaiPotential(a=0.5,b=0.0375,amp=1.,normalize=.6)
np= NFWPotential(a=4.5,normalize=.35)
hp= HernquistPotential(a=0.6/8,normalize=0.05)
ts= nu.linspace(0.,20.,10000)
mye= nu.zeros(len(e))
for ii in range(len(e)):
#Integrate the orbit
o= Orbit(vxvv[ii,:],radec=True,vo=220.,ro=8.)
o.integrate(ts,lp)
mye[ii]= o.e()
#Save
savefile= open(savefilename,'wb')
pickle.dump(mye,savefile)
pickle.dump(e,savefile)
savefile.close()
#plot
plot.bovy_print()
plot.bovy_plot(nu.array([0.,1.]),nu.array([0.,1.]),'k-',
xlabel=r'$\mathrm{Dierickx\ et\ al.}\ e$',
ylabel=r'$\mathrm{galpy}\ e$')
plot.bovy_plot(e,mye,'k,',overplot=True)
plot.bovy_end_print('myee.png')
plot.bovy_print()
plot.bovy_hist(e,bins=30,xlabel=r'$\mathrm{Dierickx\ et\ al.}\ e$')
plot.bovy_end_print('ehist.png')
plot.bovy_print()
plot.bovy_hist(mye,bins=30,xlabel=r'$\mathrm{galpy}\ e$')
plot.bovy_end_print('myehist.png')
def plot_distancedist(basesavefilename):
#First read the sample
data= apread.rcsample()
if _ADDLLOGGCUT:
data= data[data['ADDL_LOGG_CUT'] == 1]
#Histogram
bovy_plot.bovy_print(fig_width=6.)
bovy_plot.bovy_hist(data['RC_DIST'],
histtype='step',
color='k',
lw=1.5,
bins=51,
xrange=[0.,10.],
xlabel=r'$\mathrm{distance}\,(\mathrm{kpc})$',
ylabel=r'$\mathrm{Number\ of\ stars}$',zorder=10)
ax= pyplot.gca()
ax2= ax.twinx()
pyplot.sca(ax2)
bovy_plot.bovy_plot(sorted(data['RC_DIST']),
numpy.linspace(1./len(data),1.,len(data)),
'k-',
overplot=True)
ax2.set_xlim(0.,10.)
ax2.set_ylim(0.,1.)
bovy_plot._add_ticks()
ax2.set_ylabel(r'$\mathrm{cumulative\ distribution}$')
bovy_plot.bovy_end_print(basesavefilename+'_hist.'+_EXT)
#RZ
bovy_plot.bovy_print()
bovy_plot.bovy_plot(data['RC_GALR'],data['RC_GALZ'],
'k.',ms=2.,
xrange=[0.,16.],
yrange=[-3.,3.],
xlabel=r'$R\,(\mathrm{kpc})$',
ylabel=r'$Z\,(\mathrm{kpc})$',
onedhists=True)
xarr, dx=1.5, -1.
from matplotlib.patches import FancyArrowPatch
_legendsize= 16
arr= FancyArrowPatch(posA=(xarr+0.05,0.),
posB=(xarr+dx*10./8.,0.),
arrowstyle='->',
connectionstyle='arc3,rad=%4.2f' % (0.),
shrinkA=2.0, shrinkB=2.0,
mutation_scale=20.0,
mutation_aspect=None,fc='k')
ax = pyplot.gca()
ax.add_patch(arr)
bovy_plot.bovy_text(xarr+7.*dx/8.,-0.45,r'$\mathrm{GC}$',
size=_legendsize)
arr= FancyArrowPatch(posA=(1.5,-0.05),
posB=(1.5,.75),
arrowstyle='->',
connectionstyle='arc3,rad=%4.2f' % (0.),
shrinkA=2.0, shrinkB=2.0,
mutation_scale=20.0,
mutation_aspect=None,fc='k')
ax = pyplot.gca()
ax.add_patch(arr)
bovy_plot.bovy_text(1.59,0.2,r'$\mathrm{NGP}$',
size=_legendsize)
bovy_plot.bovy_end_print(basesavefilename+'_RZ.'+_EXT)
#XY
bovy_plot.bovy_print()
bovy_plot.bovy_plot(data['RC_GALR']*numpy.cos(data['RC_GALPHI']),
data['RC_GALR']*numpy.sin(data['RC_GALPHI']),
'k.',
xrange=[0.,16.],
yrange=[5.,-5.],
xlabel=r'$X_{\mathrm{GC}}\,(\mathrm{kpc})$',
ylabel=r'$Y_{\mathrm{GC}}\,(\mathrm{kpc})$',
onedhists=True,ms=2.)
xarr, dx= 2.2, -2.
arr= FancyArrowPatch(posA=(xarr,0.),
posB=(xarr+dx,0.),
arrowstyle='->',
connectionstyle='arc3,rad=%4.2f' % (0.),
shrinkA=2.0, shrinkB=2.0,
mutation_scale=20.0,
mutation_aspect=None,fc='k')
ax = pyplot.gca()
ax.add_patch(arr)
bovy_plot.bovy_text(xarr+7.*dx/8.,-0.25,r'$\mathrm{GC}$',
size=_legendsize)
xcen, ycen, dr, t= -2., 0., 4., 14.*numpy.pi/180.
arr= FancyArrowPatch(posA=(xcen+dr*numpy.cos(t),
ycen+dr*numpy.sin(t)),
posB=(xcen+dr*numpy.cos(-t),
ycen+dr*numpy.sin(-t)),
arrowstyle='<-',
connectionstyle='arc3,rad=%4.2f' % (2.*t),
shrinkA=2.0, shrinkB=2.0,
mutation_scale=20.0,
mutation_aspect=None,fc='k')
ax.add_patch(arr)
bovy_plot.bovy_end_print(basesavefilename+'_XY.'+_EXT)
def plot_mapflarepdf(savename,plotname):
# Load the samples
with open('../mapfits/tribrokenexpflare.sav','rb') as savefile:
bf= numpy.array(pickle.load(savefile))
samples= numpy.array(pickle.load(savefile))
maps= define_rcsample.MAPs()
# Loop through the low-alpha MAPs and compute the XD decomposition
if 'lowalpha' in savename:
plotmaps= [9,16,23,29,36,43,50,57,64,71]
else:
plotmaps= [19,26,32,39,45]
if not os.path.exists(savename):
ngauss= 2
allxamp= numpy.empty((len(plotmaps),ngauss))
allxmean= numpy.empty((len(plotmaps),ngauss,1))
allxcovar= numpy.empty((len(plotmaps),ngauss,1,1))
cnt= 0
for ii, map in enumerate(maps.map()):
if not ii in plotmaps: continue
print ii
# Fit PDFs with XD
xamp= numpy.array([0.45,0.5])
xmean= numpy.array([numpy.mean(samples[ii,4])
+numpy.random.normal()*numpy.std(samples[ii,4]),
numpy.mean(samples[ii,4])
+numpy.random.normal()*numpy.std(samples[ii,4])])[:,numpy.newaxis]
xcovar= numpy.reshape(numpy.tile(numpy.var(samples[ii,4]),(2,1)),
(2,1,1))
XD.extreme_deconvolution(samples[ii,4][:,numpy.newaxis],
numpy.zeros((len(samples[ii,4]),1)),
xamp,xmean,xcovar)
allxamp[cnt]= xamp
allxmean[cnt]= xmean
allxcovar[cnt]= xcovar
cnt+= 1
save_pickles(savename,allxamp,allxmean,allxcovar)
else:
with open(savename,'rb') as savefile:
allxamp= pickle.load(savefile)
allxmean= pickle.load(savefile)
allxcovar= pickle.load(savefile)
# Now plot
cmap= cm.coolwarm
xrange= [-0.37,0.25]
if 'lowalpha' in savename:
# xrange= [-0.4,0.2]
yrange= [0.,30.]
combDiv= 2.
colorFunc= lambda x: cmap((x+0.6)*0.95/0.9+0.05)
else:
# xrange= [-0.3,0.3]
yrange= [0.,13.5]
colorFunc= lambda x: cmap((x+0.5)*0.95/0.5+0.05)
combDiv= 1.5
overplot= False
plotXDFit= True
cnt= 0
bovy_plot.bovy_print(axes_labelsize=18,text_fontsize=18,
xtick_labelsize=14,ytick_labelsize=14)
for ii, map in enumerate(maps.map()):
if not ii in plotmaps: continue
tfeh= round(numpy.median(map['FE_H'])*20.)/20.
if tfeh == 0.25: tfeh= 0.3
if tfeh == -0.1: tfeh= -0.1
bovy_plot.bovy_hist(samples[ii,4],
range=xrange,bins=51,overplot=overplot,
yrange=yrange,
histtype='step',normed=True,zorder=2,
color=colorFunc(tfeh),
xlabel=r'$R_{\mathrm{flare}}^{-1}\,(\mathrm{kpc}^{-1})$')
if plotXDFit:
txs= numpy.linspace(xrange[0],xrange[1],1001)
pyplot.plot(txs,1./numpy.sqrt(2.*numpy.pi)*(allxamp[cnt,0]/numpy.sqrt(allxcovar[cnt,0,0,0])*numpy.exp(-0.5*(txs-allxmean[cnt,0,0])**2./allxcovar[cnt,0,0,0])
+allxamp[cnt,1]/numpy.sqrt(allxcovar[cnt,1,0,0])*numpy.exp(-0.5*(txs-allxmean[cnt,1,0])**2./allxcovar[cnt,1,0,0])),
color=colorFunc(tfeh),
zorder=1)
overplot=True
cnt+= 1
txs= numpy.linspace(xrange[0],xrange[1],1001)
comb= numpy.ones_like(txs)
for ii in range(len(plotmaps)):
comb*= 1./numpy.sqrt(2.*numpy.pi)*(allxamp[ii,0]/numpy.sqrt(allxcovar[ii,0,0,0])*numpy.exp(-0.5*(txs-allxmean[ii,0,0])**2./allxcovar[ii,0,0,0])
+allxamp[ii,1]/numpy.sqrt(allxcovar[ii,1,0,0])*numpy.exp(-0.5*(txs-allxmean[ii,1,0])**2./allxcovar[ii,1,0,0]))
comb/= numpy.sum(comb)*(txs[1]-txs[0])
pyplot.plot(txs,comb/combDiv,'k-',lw=2.,zorder=20)
pyplot.plot([0.,0.],[0.,50.],'k--',lw=1.5,zorder=0)
t= pyplot.text(xrange[0]+0.25*(xrange[1]-xrange[0])+0.03*('highalpha' in savename),
0.8*yrange[1],
r'$R_{\mathrm{flare}}^{-1} = %.2f \pm %.2f\,\mathrm{kpc}^{-1}$' % (numpy.sum(comb*txs)/numpy.sum(comb), numpy.sqrt(numpy.sum(comb*txs**2.)/numpy.sum(comb)-(numpy.sum(comb*txs)/numpy.sum(comb))**2.)),
size=18.)
t.set_bbox(dict(color='w',edgecolor='none'))
if 'lowalpha' in savename:
bovy_plot.bovy_text(r'$\mathrm{low-}[\alpha/\mathrm{Fe}]\ \mathrm{MAPs}$',
top_left=True,
size=16.)
else:
bovy_plot.bovy_text(r'$\mathrm{high-}[\alpha/\mathrm{Fe}]\ \mathrm{MAPs}$',
top_left=True,
size=16.)
bovy_plot.bovy_end_print(plotname)
return None
def plot_stream_aa(plotfilename):
#Read stream
data= numpy.loadtxt(os.path.join(_STREAMSNAPAADIR,
'gd1_evol_hitres_aa_01312.dat'),
delimiter=',')
includeorbit= True
includetrack= True
fmt= 'k,'
if includeorbit:
#Read progenitor actions
progfile= '../sim/gd1_evol_hitres_progaai.dat'
progaa= numpy.loadtxt(progfile,delimiter=',')
if includetrack:
#Setup stream model
lp= potential.LogarithmicHaloPotential(q=0.9,normalize=1.)
aAI= actionAngleIsochroneApprox(b=0.8,pot=lp)
obs= numpy.array([1.56148083,0.35081535,-1.15481504,
0.88719443,-0.47713334,0.12019596])
sdf= streamdf(_SIGV/220.,progenitor=Orbit(obs),pot=lp,aA=aAI,
leading=True,nosetup=True,
tdisrupt=4.5/bovy_conversion.time_in_Gyr(220.,8.))
if 'araz' in plotfilename:
thetar= data[:,6]
thetar= (numpy.pi+(thetar-numpy.median(thetar))) % (2.*numpy.pi)
indx= numpy.fabs(thetar-numpy.pi) > (5.*numpy.median(numpy.fabs(thetar-numpy.median(thetar))))
plotx= data[indx,6]
ploty= data[indx,8]
plotx= (numpy.pi+(plotx-numpy.median(data[:,6]))) % (2.*numpy.pi)
ploty= (numpy.pi+(ploty-numpy.median(data[:,8]))) % (2.*numpy.pi)
xrange=[numpy.pi-1.,numpy.pi+1.]
yrange=[numpy.pi-1.,numpy.pi+1.]
xlabel=r'$\theta_R$'
ylabel=r'$\theta_Z$'
elif 'arap' in plotfilename and not 'aparaperp' in plotfilename:
thetar= data[:,6]
thetar= (numpy.pi+(thetar-numpy.median(thetar))) % (2.*numpy.pi)
indx= numpy.fabs(thetar-numpy.pi) > (5.*numpy.median(numpy.fabs(thetar-numpy.median(thetar))))
plotx= data[indx,6]
ploty= data[indx,7]
plotx= (numpy.pi+(plotx-numpy.median(data[:,6]))) % (2.*numpy.pi)
ploty= (numpy.pi+(ploty-numpy.median(data[:,7]))) % (2.*numpy.pi)
xrange=[numpy.pi-1.,numpy.pi+1.]
yrange=[numpy.pi-1.,numpy.pi+1.]
xlabel=r'$\theta_R$'
ylabel=r'$\theta_\phi$'
elif 'oroz' in plotfilename:
thetar= data[:,6]
thetar= (numpy.pi+(thetar-numpy.median(thetar))) % (2.*numpy.pi)
indx= numpy.fabs(thetar-numpy.pi) > (5.*numpy.median(numpy.fabs(thetar-numpy.median(thetar))))
plotx= data[indx,3]*bovy_conversion.freq_in_Gyr(220.,8.)
ploty= data[indx,5]*bovy_conversion.freq_in_Gyr(220.,8.)
xrange=[15.45,15.95]
yrange=[11.7,12.05]
xlabel=r'$\Omega_R\,(\mathrm{Gyr}^{-1})$'
ylabel=r'$\Omega_Z\,(\mathrm{Gyr}^{-1})$'
elif 'orop' in plotfilename:
thetar= data[:,6]
thetar= (numpy.pi+(thetar-numpy.median(thetar))) % (2.*numpy.pi)
indx= numpy.fabs(thetar-numpy.pi) > (5.*numpy.median(numpy.fabs(thetar-numpy.median(thetar))))
plotx= data[indx,3]*bovy_conversion.freq_in_Gyr(220.,8.)
ploty= data[indx,4]*bovy_conversion.freq_in_Gyr(220.,8.)
xrange=[15.45,15.95]
yrange=[-10.98,-10.65]
xlabel=r'$\Omega_R\,(\mathrm{Gyr}^{-1})$'
ylabel=r'$\Omega_\phi\,(\mathrm{Gyr}^{-1})$'
elif 'jrjz' in plotfilename:
thetar= data[:,6]
thetar= (numpy.pi+(thetar-numpy.median(thetar))) % (2.*numpy.pi)
indx= numpy.fabs(thetar-numpy.pi) > (5.*numpy.median(numpy.fabs(thetar-numpy.median(thetar))))
plotx= data[indx,0]*8.
ploty= data[indx,2]*8.
xrange=[1.2,1.42]
yrange=[3.98,4.18]
xlabel=r'$J_R\,(220\,\mathrm{km\,s}^{-1}\,\mathrm{kpc})$'
ylabel=r'$J_Z\,(220\,\mathrm{km\,s}^{-1}\,\mathrm{kpc})$'
elif 'jrjp' in plotfilename:
thetar= data[:,6]
thetar= (numpy.pi+(thetar-numpy.median(thetar))) % (2.*numpy.pi)
indx= numpy.fabs(thetar-numpy.pi) > (5.*numpy.median(numpy.fabs(thetar-numpy.median(thetar))))
plotx= data[indx,0]*8.
ploty= data[indx,1]*8.
xrange=[1.2,1.42]
yrange=[-14.64,-14.23]
xlabel=r'$J_R\,(220\,\mathrm{km\,s}^{-1}\,\mathrm{kpc})$'
ylabel=r'$L_Z\,(220\,\mathrm{km\,s}^{-1}\,\mathrm{kpc})$'
elif 'dohist' in plotfilename:
thetar= data[:,6]
thetar= (numpy.pi+(thetar-numpy.median(thetar))) % (2.*numpy.pi)
indx= numpy.fabs(thetar-numpy.pi) > (5.*numpy.median(numpy.fabs(thetar-numpy.median(thetar))))
#Frequencies
Or= data[:,3]
Op= data[:,4]
Oz= data[:,5]
dOr= Or[indx]-numpy.median(Or)
dOp= Op[indx]-numpy.median(Op)
dOz= Oz[indx]-numpy.median(Oz)
dO= numpy.vstack((dOr,dOp,dOz))*bovy_conversion.freq_in_Gyr(220.,8.)
dO4dir= copy.copy(dO)
dO4dir[:,dO4dir[:,0] < 0.]*= -1.
dOdir= numpy.median(dO4dir,axis=1)
dOdir/= numpy.sqrt(numpy.sum(dOdir**2.))
dO1d= numpy.dot(dOdir,dO)
print "Misalignment:", numpy.arccos(numpy.sum(dOdir*progaa[-1,3:6])/numpy.sqrt(numpy.sum(dOdir**2.)*numpy.sum(progaa[-1,3:6]**2.)))/numpy.pi*180.-180.
dO1d[dO1d < 0.]*= -1.
bovy_plot.bovy_print()
bovy_plot.bovy_hist(dO1d,range=[0.,0.4],bins=61,
normed=True,
xlabel=r'$\Large|\Delta \mathbf{\Omega}_\parallel\Large|\,(\mathrm{Gyr}^{-1})$',
histtype='step',color='k',zorder=10)
#Overplot best-fit Gaussian
xs= numpy.linspace(0.,0.4,1001)
print numpy.mean(dO1d), numpy.std(dO1d)
bovy_plot.bovy_plot(xs,1./numpy.sqrt(2.*numpy.pi)/numpy.std(dO1d)\
*numpy.exp(-(xs-numpy.mean(dO1d))**2./2./numpy.var(dO1d)),
'--',color='k',overplot=True,lw=2.,zorder=2)
bestfit= optimize.fmin_powell(gausstimesvalue,
numpy.array([numpy.log(numpy.mean(dO1d)*2.),
numpy.log(numpy.std(dO1d))]),
args=(dO1d,))
print numpy.exp(bestfit)
bovy_plot.bovy_plot(xs,gausstimesvalue(bestfit,xs,nologsum=True),
'-',color='0.4',overplot=True,lw=2.,zorder=1)
bovy_plot.bovy_end_print(plotfilename)
return None
elif 'dahist' in plotfilename:
thetar= data[:,6]
thetar= (numpy.pi+(thetar-numpy.median(thetar))) % (2.*numpy.pi)
indx= numpy.fabs(thetar-numpy.pi) > (5.*numpy.median(numpy.fabs(thetar-numpy.median(thetar))))
thetar= thetar[indx]
thetap= data[:,7]
thetap= (numpy.pi+(thetap-numpy.median(thetap))) % (2.*numpy.pi)
thetap= thetap[indx]
thetaz= data[:,8]
thetaz= (numpy.pi+(thetaz-numpy.median(thetaz))) % (2.*numpy.pi)
thetaz= thetaz[indx]
#center around 0 (instead of pi)
thetar-= numpy.pi
thetap-= numpy.pi
thetaz-= numpy.pi
#Frequencies
Or= data[:,3]
Op= data[:,4]
Oz= data[:,5]
dOr= Or[indx]-numpy.median(Or)
dOp= Op[indx]-numpy.median(Op)
dOz= Oz[indx]-numpy.median(Oz)
dO= numpy.vstack((dOr,dOp,dOz))*bovy_conversion.freq_in_Gyr(220.,8.)
#Direction in which the stream spreads
dO4dir= copy.copy(dO)
dO4dir[:,dO4dir[:,0] < 0.]*= -1.
dOdir= numpy.median(dO4dir,axis=1)
dOdir/= numpy.sqrt(numpy.sum(dOdir**2.))
#Gram-Schmidt to get the perpendicular directions
v2= numpy.array([1.,0.,0.])
v3= numpy.array([0.,1.,0.])
u2= v2-numpy.sum(dOdir*v2)*dOdir
u2/= numpy.sqrt(numpy.sum(u2**2.))
u3= v3-numpy.sum(dOdir*v3)*dOdir-numpy.sum(u2*v3)*u2
#Times
dangle= numpy.vstack((thetar,thetap,thetaz))
dts= numpy.sum(dO*dangle,axis=0)/numpy.sum(dO**2.,axis=0)
#Rewind angles
dangle-= dO*dts
newdangle= numpy.empty_like(dangle)
newdangle[0,:]= numpy.dot(dOdir,dangle)
newdangle[1,:]= numpy.dot(u2,dangle)
newdangle[2,:]= numpy.dot(u3,dangle)
bovy_plot.bovy_print()
xmin= -0.015
bovy_plot.bovy_hist(newdangle[2,:].flatten(),range=[xmin,-xmin],bins=61,
xlabel=r'$|\Delta \mathbf{\theta}|$',
normed=True,lw=2.,
color='k',zorder=10,
histtype='step')
#Overplot best-fit Gaussian
xs= numpy.linspace(xmin,-xmin,1001)
bovy_plot.bovy_plot(xs,1./numpy.sqrt(2.*numpy.pi)/numpy.std(newdangle[1:,:])\
*numpy.exp(-(xs-numpy.mean(newdangle[1:,:]))**2./2./numpy.var(newdangle[1:,:])),
'--',color='k',overplot=True,lw=2.,zorder=0)
print "along", numpy.mean(newdangle[0,:]), numpy.std(newdangle[0,:])
print "perpendicular 1", numpy.mean(newdangle[1,:]), numpy.std(newdangle[1,:])
print "perpendicular 2", numpy.mean(newdangle[2,:]), numpy.std(newdangle[2,:])
bovy_plot.bovy_end_print(plotfilename)
return None
elif 'aparopar' in plotfilename:
thetar= data[:,6]
thetar= (numpy.pi+(thetar-numpy.median(thetar))) % (2.*numpy.pi)
indx= numpy.fabs(thetar-numpy.pi) > (5.*numpy.median(numpy.fabs(thetar-numpy.median(thetar))))
thetar= thetar[indx]
thetap= data[:,7]
thetap= (numpy.pi+(thetap-numpy.median(thetap))) % (2.*numpy.pi)
thetap= thetap[indx]
thetaz= data[:,8]
thetaz= (numpy.pi+(thetaz-numpy.median(thetaz))) % (2.*numpy.pi)
thetaz= thetaz[indx]
#center around 0 (instead of pi)
thetar-= numpy.pi
thetap-= numpy.pi
thetaz-= numpy.pi
#Frequencies
Or= data[:,3]
Op= data[:,4]
Oz= data[:,5]
dOr= Or[indx]-numpy.median(Or)
dOp= Op[indx]-numpy.median(Op)
dOz= Oz[indx]-numpy.median(Oz)
dO= numpy.vstack((dOr,dOp,dOz))*bovy_conversion.freq_in_Gyr(220.,8.)
#Direction in which the stream spreads
dO4dir= copy.copy(dO)
dO4dir[:,dO4dir[:,0] < 0.]*= -1.
dOdir= numpy.median(dO4dir,axis=1)
dOdir/= numpy.sqrt(numpy.sum(dOdir**2.))
#Times
dangle= numpy.vstack((thetar,thetap,thetaz))
plotx= numpy.fabs(numpy.dot(dangle.T,dOdir))
ploty= numpy.fabs(numpy.dot(dO.T,dOdir))
xrange=[0.,1.3]
yrange=[0.1,0.3]
xlabel= r'$\Large|\Delta \mathbf{\theta}_\parallel\Large|$'
ylabel= r'$\Large|\Delta \mathbf{\Omega}_\parallel\Large|\,(\mathrm{Gyr}^{-1})$'
fmt= 'k.'
elif 'aparoperp' in plotfilename:
thetar= data[:,6]
thetar= (numpy.pi+(thetar-numpy.median(thetar))) % (2.*numpy.pi)
indx= numpy.fabs(thetar-numpy.pi) > (5.*numpy.median(numpy.fabs(thetar-numpy.median(thetar))))
thetar= thetar[indx]
thetap= data[:,7]
thetap= (numpy.pi+(thetap-numpy.median(thetap))) % (2.*numpy.pi)
thetap= thetap[indx]
thetaz= data[:,8]
thetaz= (numpy.pi+(thetaz-numpy.median(thetaz))) % (2.*numpy.pi)
thetaz= thetaz[indx]
#center around 0 (instead of pi)
thetar-= numpy.pi
thetap-= numpy.pi
thetaz-= numpy.pi
#Frequencies
Or= data[:,3]
Op= data[:,4]
Oz= data[:,5]
dOr= Or[indx]-numpy.median(Or)
dOp= Op[indx]-numpy.median(Op)
dOz= Oz[indx]-numpy.median(Oz)
dO= numpy.vstack((dOr,dOp,dOz))*bovy_conversion.freq_in_Gyr(220.,8.)
#Direction in which the stream spreads
dO4dir= copy.copy(dO)
dO4dir[:,dO4dir[:,0] < 0.]*= -1.
dOdir= numpy.median(dO4dir,axis=1)
dOdir/= numpy.sqrt(numpy.sum(dOdir**2.))
#Times
dangle= numpy.vstack((thetar,thetap,thetaz))
plotx= numpy.fabs(numpy.dot(dangle.T,dOdir))
ploty= numpy.sqrt(numpy.sum(dO**2.,axis=0)\
-(numpy.dot(dO.T,dOdir))**2.)
print numpy.std(ploty)
xrange=[0.,1.3]
yrange=[0.,0.005]
xlabel= r'$\Large|\Delta \mathbf{\theta}_\parallel\Large|$'
ylabel= r'$\Large|\Delta \mathbf{\Omega}_\perp\Large|\,(\mathrm{Gyr}^{-1})$'
fmt= 'k.'
elif 'aparaperp' in plotfilename:
thetar= data[:,6]
thetar= (numpy.pi+(thetar-numpy.median(thetar))) % (2.*numpy.pi)
indx= numpy.fabs(thetar-numpy.pi) > (5.*numpy.median(numpy.fabs(thetar-numpy.median(thetar))))
thetar= thetar[indx]
thetap= data[:,7]
thetap= (numpy.pi+(thetap-numpy.median(thetap))) % (2.*numpy.pi)
thetap= thetap[indx]
thetaz= data[:,8]
thetaz= (numpy.pi+(thetaz-numpy.median(thetaz))) % (2.*numpy.pi)
thetaz= thetaz[indx]
#center around 0 (instead of pi)
thetar-= numpy.pi
thetap-= numpy.pi
thetaz-= numpy.pi
#Frequencies
Or= data[:,3]
Op= data[:,4]
Oz= data[:,5]
dOr= Or[indx]-numpy.median(Or)
dOp= Op[indx]-numpy.median(Op)
dOz= Oz[indx]-numpy.median(Oz)
dO= numpy.vstack((dOr,dOp,dOz))*bovy_conversion.freq_in_Gyr(220.,8.)
#Direction in which the stream spreads
dO4dir= copy.copy(dO)
dO4dir[:,dO4dir[:,0] < 0.]*= -1.
dOdir= numpy.median(dO4dir,axis=1)
dOdir/= numpy.sqrt(numpy.sum(dOdir**2.))
#Times
dangle= numpy.vstack((thetar,thetap,thetaz))
plotx= numpy.fabs(numpy.dot(dangle.T,dOdir))
ploty= numpy.sqrt(numpy.sum(dangle**2.,axis=0)\
-(numpy.dot(dangle.T,dOdir))**2.)
xrange=[0.,1.3]
yrange=[0.,0.03]
xlabel= r'$\Large|\Delta \mathbf{\theta}_\parallel\Large|$'
ylabel= r'$\Large|\Delta \mathbf{\theta}_\perp\Large|$'
fmt= 'k.'
elif 'apartime' in plotfilename:
thetar= data[:,6]
thetar= (numpy.pi+(thetar-numpy.median(thetar))) % (2.*numpy.pi)
indx= numpy.fabs(thetar-numpy.pi) > (5.*numpy.median(numpy.fabs(thetar-numpy.median(thetar))))
thetar= thetar[indx]
thetap= data[:,7]
thetap= (numpy.pi+(thetap-numpy.median(thetap))) % (2.*numpy.pi)
thetap= thetap[indx]
thetaz= data[:,8]
thetaz= (numpy.pi+(thetaz-numpy.median(thetaz))) % (2.*numpy.pi)
thetaz= thetaz[indx]
#center around 0 (instead of pi)
thetar-= numpy.pi
thetap-= numpy.pi
thetaz-= numpy.pi
#Frequencies
Or= data[:,3]
Op= data[:,4]
Oz= data[:,5]
dOr= Or[indx]-numpy.median(Or)
dOp= Op[indx]-numpy.median(Op)
dOz= Oz[indx]-numpy.median(Oz)
dO= numpy.vstack((dOr,dOp,dOz))*bovy_conversion.freq_in_Gyr(220.,8.)
#Direction in which the stream spreads
dO4dir= copy.copy(dO)
dO4dir[:,dO4dir[:,0] < 0.]*= -1.
dOdir= numpy.median(dO4dir,axis=1)
dOdir/= numpy.sqrt(numpy.sum(dOdir**2.))
#Times
dangle= numpy.vstack((thetar,thetap,thetaz))
dts= numpy.sum(dO*dangle,axis=0)/numpy.sum(dO**2.,axis=0)
plotx= numpy.fabs(numpy.dot(dangle.T,dOdir))
ploty= dts
xrange=[0.,1.3]
yrange=[0.,5.]
xlabel= r'$\Large|\Delta \mathbf{\theta}_\parallel\Large|$'
ylabel= r'$t_s\,(\mathrm{Gyr})$'
fmt= 'k.'
bovy_plot.bovy_print()
bovy_plot.bovy_plot(plotx,ploty,fmt,
xlabel=xlabel,
ylabel=ylabel,
xrange=xrange,
yrange=yrange,zorder=5)
if includeorbit and 'araz' in plotfilename:
#plot frequency line
xs= numpy.array(xrange)
ys= (xs-numpy.pi)*progaa[-1,5]/progaa[-1,3]+numpy.pi
bovy_plot.bovy_plot(xs,ys,'k--',overplot=True,
zorder=0)
elif includeorbit and 'arap' in plotfilename:
#plot frequency line
xs= numpy.array(xrange)
ys= (xs-numpy.pi)*progaa[-1,4]/progaa[-1,3]+numpy.pi
bovy_plot.bovy_plot(xs,ys,'k--',overplot=True,
zorder=0)
elif includeorbit and 'oroz' in plotfilename:
bovy_plot.bovy_plot(progaa[-1,3]*bovy_conversion.freq_in_Gyr(220.,8.),
progaa[-1,5]*bovy_conversion.freq_in_Gyr(220.,8.),
'o',overplot=True,color='0.5',
mec='none',ms=8.,
zorder=0)
elif includeorbit and 'orop' in plotfilename:
bovy_plot.bovy_plot(progaa[-1,3]*bovy_conversion.freq_in_Gyr(220.,8.),
progaa[-1,4]*bovy_conversion.freq_in_Gyr(220.,8.),
'o',overplot=True,color='0.5',
mec='none',ms=8.,
zorder=0)
elif includeorbit and 'jrjz' in plotfilename:
bovy_plot.bovy_plot(progaa[-1,0]*8.,
progaa[-1,2]*8.,
'o',overplot=True,color='0.5',
mec='none',ms=8.,
zorder=0)
elif includeorbit and 'jrjp' in plotfilename:
bovy_plot.bovy_plot(progaa[-1,0]*8.,
progaa[-1,1]*8.,
'o',overplot=True,color='0.5',
mec='none',ms=8.,
zorder=0)
if includetrack and 'aparopar' in plotfilename:
#Calculate mean and std of Omegapar as a function of anglepar
das= numpy.linspace(0.,1.3,1001)
dOs= numpy.array([sdf.meanOmega(da,oned=True) for da in das])
sOs= numpy.array([sdf.sigOmega(da) for da in das])
bovy_plot.bovy_plot(das,dOs*bovy_conversion.freq_in_Gyr(220.,8),
'-',color='0.75',lw=2.,overplot=True,zorder=10)
pyplot.fill_between(das,(dOs+sOs)*bovy_conversion.freq_in_Gyr(220.,8),
(dOs-sOs)*bovy_conversion.freq_in_Gyr(220.,8),
color='0.6',zorder=1)
pyplot.fill_between(das,(dOs+2*sOs)*bovy_conversion.freq_in_Gyr(220.,8),
(dOs-2*sOs)*bovy_conversion.freq_in_Gyr(220.,8),
color='0.8',zorder=0)
#Also plot the apar at which t_d becomes important
pyplot.plot([sdf.meanOmega(0.01,oned=True)*sdf._tdisrupt,
sdf.meanOmega(0.01,oned=True)*sdf._tdisrupt],
[0.,0.3],
'k--')
elif includetrack and 'apartime' in plotfilename:
das= numpy.linspace(0.01,1.3,101)
mts= numpy.array([sdf.meantdAngle(da) for da in das])
sts= numpy.array([sdf.sigtdAngle(da) for da in das])
bovy_plot.bovy_plot(das,mts*bovy_conversion.time_in_Gyr(220.,8),
'-',color='0.75',lw=2.,overplot=True,zorder=10)
pyplot.fill_between(das,(mts+2*sts)*bovy_conversion.time_in_Gyr(220.,8),
(mts-2*sts)*bovy_conversion.time_in_Gyr(220.,8),
color='0.8',zorder=0)
pyplot.fill_between(das,(mts+sts)*bovy_conversion.time_in_Gyr(220.,8),
(mts-sts)*bovy_conversion.time_in_Gyr(220.,8),
color='0.6',zorder=1)
elif includetrack and 'aparaperp' in plotfilename:
das= numpy.linspace(0.01,1.3,101)
sas= numpy.array([sdf.sigangledAngle(da) for da in das])*numpy.sqrt(2.)
sass= numpy.array([sdf.sigangledAngle(da,simple=True) for da in das])*numpy.sqrt(2.)
pyplot.fill_between(das,0.,
(2*sas),
color='0.8',zorder=0)
pyplot.fill_between(das,0.,
(sas),
color='0.6',zorder=1)
pyplot.plot(das,sass,'--',color='w',zorder=7,lw=2.)
#ax= pyplot.gca()
#ax.set_rasterized(True)
bovy_plot.bovy_end_print(plotfilename)
def main(args: Optional[list] = None,
opts: Optional[argparse.Namespace] = None):
"""Fit MWPotential with Double-Exponential Disk Script Function.
Parameters
----------
args : list, optional
an optional single argument that holds the sys.argv list,
except for the script name (e.g., argv[1:])
"""
if opts is not None and args is None:
pass
else:
parser = make_parser()
opts = parser.parse_args(args)
fpath = opts.fpath + "/" if not opts.fpath.endswith("/") else opts.fpath
opath = opts.opath + "/" if not opts.opath.endswith("/") else opts.opath
# -------------------------------------------------------------------------
# Using an exponential disk instead of a Miyamoto-Nagai disk
# -----------------------
# $c=1$:
plt.figure(figsize=(16, 5))
p_exp = mw_pot.fit(
fitc=False,
c=1.0,
dblexp=True,
plots=fpath + "Clemens-c_1.pdf",
)
# -----------------------
# $c=0.5$:
plt.figure(figsize=(16, 5))
p_exp = mw_pot.fit(
fitc=False,
c=0.5,
dblexp=True,
plots=fpath + "Clemens-c_0p5.pdf",
)
# -----------------------
# $c=1.5$:
plt.figure(figsize=(16, 5))
p_exp = mw_pot.fit(
fitc=False,
c=1.5,
dblexp=True,
plots=fpath + "Clemens-c_1p5.pdf",
)
# -----------------------
# leave c free
plt.figure(figsize=(16, 5))
p_exp_cfree = mw_pot.fit(
fitc=True,
c=None,
dblexp=True,
plots=fpath + "Clemens-c_free.pdf",
)
# -----------------------
bf_savefilename = opath + "mwpot14varyc-dblexp-bf.pkl"
if os.path.exists(bf_savefilename):
with open(bf_savefilename, "rb") as savefile:
cs = pickle.load(savefile)
bf_params = pickle.load(savefile)
else:
cs = np.arange(0.5, 4.1, 0.1)
bf_params = []
for c in tqdm(cs):
dum = mw_pot.fit(
fitc=False,
c=c,
dblexp=True,
plots=fpath + "mwpot14varyc-dblexp-bf-fit.pdf",
)
bf_params.append(dum[0])
save_pickles(bf_savefilename, cs, bf_params)
# -----------------------
samples_savefilename = opath + "mwpot14varyc-dblexp-samples.pkl"
if os.path.exists(samples_savefilename):
with open(samples_savefilename, "rb") as savefile:
s = pickle.load(savefile)
else:
s = mw_pot.sample(
nsamples=100000,
params=p_exp_cfree[0],
fitc=True,
c=None,
plots=fpath + "mwpot14varyc-dblexp-samples.pdf",
dblexp=True,
)
save_pickles(samples_savefilename, s)
# -----------------------
plt.figure()
mw_pot.plot_samples(
s,
True,
False,
savefig=fpath + "varyc-dblexp-samples-corner.pdf",
)
# -----------------------
plt.figure()
bovy_plot.bovy_print(
axes_labelsize=17.0,
text_fontsize=12.0,
xtick_labelsize=15.0,
ytick_labelsize=15.0,
)
su.plot_mcmc_c(
s,
False,
cs,
bf_params,
savefig=fpath + "varyc-dblexp-samples-dependence.pdf",
)
# -----------------------
plt.figure(figsize=(4, 4))
dum = bovy_plot.bovy_hist(
s[7],
bins=36,
histtype="step",
lw=2.0,
xlabel=r"$c/a$",
xrange=[0.0, 4.0],
normed=True,
)
sortedc = np.array(sorted(s[7]))
plt.title("2.5%% and 0.5%% lower limits: %.2f, %.2f" % (
sortedc[int(np.floor(0.025 * len(sortedc)))],
sortedc[int(np.floor(0.005 * len(sortedc)))],
))
plt.savefig(fpath + "varyc-dblexp-samples-shape_hist.pdf")
# -----------------------
# Also fitting $R_0$ and $V_c(R_0)$
plt.figure(figsize=(16, 5))
p_exp_cfree_voro = mw_pot.fit(
fitc=True,
c=None,
dblexp=True,
fitvoro=True,
plots=fpath + "fitvoro-samples.pdf",
)
# -----------------------
samples_savefilename = opath + "mwpot14varyc-dblexp-fitvoro-samples.pkl"
if os.path.exists(samples_savefilename):
with open(samples_savefilename, "rb") as savefile:
s = pickle.load(savefile)
else:
s = mw_pot.sample(
nsamples=100000,
params=p_exp_cfree_voro[0],
fitc=True,
c=None,
plots=fpath + "mwpot14varyc-dblexp-fitvoro-samples.pdf",
dblexp=True,
fitvoro=True,
)
save_pickles(samples_savefilename, s)
# -----------------------
plt.figure()
mw_pot.plot_samples(
s,
True,
True,
savefig=fpath + "varyc-dblexp-fitvoro-samples-corner.pdf",
)
# -----------------------
plt.figure()
bovy_plot.bovy_print(
axes_labelsize=17.0,
text_fontsize=12.0,
xtick_labelsize=15.0,
ytick_labelsize=15.0,
)
su.plot_mcmc_c(
s,
True,
cs,
bf_params,
savefig=fpath + "varyc-dblexp-fitvoro-samples-dependence.pdf",
)
# -----------------------
plt.figure(figsize=(4, 4))
dum = bovy_plot.bovy_hist(
s[9],
bins=36,
histtype="step",
lw=2.0,
xlabel=r"$c/a$",
xrange=[0.0, 4.0],
normed=True,
)
sortedc = np.array(sorted(s[9]))
plt.title("2.5%% and 0.5%% lower limits: %.2f, %.2f" % (
sortedc[int(np.floor(0.025 * len(sortedc)))],
sortedc[int(np.floor(0.005 * len(sortedc)))],
))
plt.savefig(fpath + "varyc-dblexp-fitvoro-samples-samples-shape_hist.pdf")
# -----------------------
# Also adding in a gas disk (and still also fitting $R_0$ and $V_c(R_0)$)
plt.figure(figsize=(16, 5))
p_exp_cfree_voro_wgas = mw_pot.fit(
fitc=True,
c=None,
dblexp=True,
fitvoro=True,
addgas=True,
plots=fpath + "varyc-dblexp-fitvoro-addgas.pdf",
)
# -----------------------
samples_savefilename = "mwpot14varyc-dblexp-fitvoro-addgas-samples.pkl"
if os.path.exists(samples_savefilename):
with open(samples_savefilename, "rb") as savefile:
s = pickle.load(savefile)
else:
s = mw_pot.sample_multi(
nsamples=100000,
params=p_exp_cfree_voro_wgas[0],
fitc=True,
c=None,
plots=fpath + "mwpot14varyc-dblexp-fitvoro-addgas-samples.pdf",
dblexp=True,
fitvoro=True,
addgas=True,
)
save_pickles(samples_savefilename, s)
# -----------------------
plt.figure()
mw_pot.plot_samples(
s,
True,
True,
savefig=fpath + "varyc-dblexp-fitvoro-addgas-samples-corner.pdf",
)
# -----------------------
plt.figure()
bovy_plot.bovy_print(
axes_labelsize=17.0,
text_fontsize=12.0,
xtick_labelsize=15.0,
ytick_labelsize=15.0,
)
su.plot_mcmc_c(
s,
True,
cs,
bf_params,
savefig=fpath + "varyc-dblexp-fitvoro-addgas-samples-dependence.pdf",
)
# -----------------------
plt.figure(figsize=(4, 4))
dum = bovy_plot.bovy_hist(
s[9],
bins=36,
histtype="step",
lw=2.0,
xlabel=r"$c/a$",
xrange=[0.0, 4.0],
normed=True,
)
sortedc = np.array(sorted(s[9]))
plt.title("2.5%% and 0.5%% lower limits: %.2f, %.2f" % (
sortedc[int(np.floor(0.025 * len(sortedc)))],
sortedc[int(np.floor(0.005 * len(sortedc)))],
))
plt.savefig(fpath + "varyc-dblexp-fitvoro-addgas-samples-shape_hist.pdf")
def main(args: Optional[list] = None,
opts: Optional[argparse.Namespace] = None):
"""Fit GD1 to MW Potential Script Function.
Parameters
----------
args : list, optional
an optional single argument that holds the sys.argv list,
except for the script name (e.g., argv[1:])
"""
if opts is not None and args is None:
pass
else:
parser = make_parser()
opts = parser.parse_args(args)
fpath = opts.fpath + "/" if not opts.fpath.endswith("/") else opts.fpath
opath = opts.opath + "/" if not opts.opath.endswith("/") else opts.opath
# -----------------------
# Adding in the force measurements from GD-1; also fitting $R_0$ and $V_c(R_0)$
plt.figure(figsize=(16, 5))
p_b15_gd1_voro = mw_pot.fit(
fitc=True,
c=None,
addgd1=True,
fitvoro=True,
mc16=True,
plots=fpath + "fit.pdf",
)
# -----------------------
samples_savefilename = opath + "mwpot14varyc-fitvoro-gd1-samples.pkl"
if os.path.exists(samples_savefilename):
with open(samples_savefilename, "rb") as savefile:
s = pickle.load(savefile)
else:
s = mw_pot.sample(
nsamples=100000,
params=p_b15_gd1_voro[0],
fitc=True,
c=None,
plots=fpath + "mwpot14varyc-fitvoro-gd1-samples.pdf",
addgd1=True,
fitvoro=True,
)
save_pickles(samples_savefilename, s)
# -----------------------
plt.figure()
mw_pot.plot_samples(
s,
True,
True,
addgd1=True,
savefig=fpath + "mwpot14varyc-fitvoro-gd1-samples-corner.pdf",
)
# -----------------------
bf_savefilename = opath + "mwpot14varyc-bf.pkl" # should already exist
if os.path.exists(bf_savefilename):
with open(bf_savefilename, "rb") as savefile:
cs = pickle.load(savefile)
bf_params = pickle.load(savefile)
else:
cs = np.arange(0.5, 4.1, 0.1)
bf_params = []
for c in tqdm(cs):
dum = mw_pot.fit(fitc=False,
c=c,
plots=fpath + "mwpot14varyc-bf-fit.pdf")
bf_params.append(dum[0])
save_pickles(bf_savefilename, cs, bf_params)
# -----------------------
plt.figure()
bovy_plot.bovy_print(
axes_labelsize=17.0,
text_fontsize=12.0,
xtick_labelsize=15.0,
ytick_labelsize=15.0,
)
su.plot_mcmc_c(
s,
True,
cs,
bf_params,
savefig=fpath + "mwpot14varyc-fitvoro-gd1-samples-dependence.pdf",
)
# -----------------------
plt.figure(figsize=(4, 4))
cindx = 9
dum = bovy_plot.bovy_hist(
s[cindx],
bins=36,
histtype="step",
lw=2.0,
xlabel=r"$c/a$",
xrange=[0.5, 2.5],
normed=True,
)
plt.savefig(fpath + "mwpot14varyc-fitvoro-gd1-shape_hist")
# -----------------------
with open(opath + "fit_potential_gd1.txt", "w") as file:
sortedc = np.array(sorted(s[cindx]))
file.write("2.5%% and 0.5%% lower limits: %.2f, %.2f" % (
sortedc[int(np.floor(0.025 * len(sortedc)))],
sortedc[int(np.floor(0.005 * len(sortedc)))],
))
file.write("2.5%% and 0.5%% upper limits: %.2f, %.2f" % (
sortedc[int(np.floor(0.975 * len(sortedc)))],
sortedc[int(np.floor(0.995 * len(sortedc)))],
))
file.write("Median, 68%% confidence: %.2f, %.2f, %.2f" % (
np.median(sortedc),
sortedc[int(np.floor(0.16 * len(sortedc)))],
sortedc[int(np.floor(0.84 * len(sortedc)))],
))
def main(args: Optional[list] = None, opts: Optional[Namespace] = None):
"""Fit MWPotential2014 Script Function.
Parameters
----------
args : list, optional
an optional single argument that holds the sys.argv list,
except for the script name (e.g., argv[1:])
opts : Namespace, optional
pre-constructed results of parsed args
if not None, used ONLY if args is None
"""
if opts is not None and args is None:
pass
else:
if opts is not None:
warnings.warn("Not using `opts` because `args` are given")
parser = make_parser()
opts = parser.parse_args(args)
fpath = opts.fpath + "/" if not opts.fpath.endswith("/") else opts.fpath
opath = opts.opath + "/" if not opts.opath.endswith("/") else opts.opath
# plot chains
print("Plotting Chains")
fig = mcmc_util.plot_chains(opath)
fig.savefig(fpath + "chains.pdf")
plt.close(fig)
print("Need to continue chains:", end=" ")
for i in range(32):
ngood = mcmc_util.determine_nburn(
filename=opath + f"mwpot14-fitsigma-{i:02}.dat", return_nsamples=True,
)
if ngood < 4000:
print(f"{i:02} (N={ngood})", end=", ")
###############################################################
# RESULTING PDFS
data, _, weights, _ = read_mcmc(nburn=None, skip=1, evi_func=lambda x: 1.0)
plot_corner(data, weights=weights)
plt.savefig("figures/PDFs/corner.pdf")
plt.close()
# --------------
savefilename = "output/pal5_forces_mcmc.pkl"
if not os.path.exists(savefilename):
data_wf, index_wf, weights_wf, evi_wf = read_mcmc(
nburn=None, addforces=True, skip=1, evi_func=lambda x: 1.0
)
save_pickles(savefilename, data_wf, index_wf, weights_wf, evi_wf)
else:
with open(savefilename, "rb") as savefile:
data_wf = pickle.load(savefile)
index_wf = pickle.load(savefile)
weights_wf = pickle.load(savefile)
evi_wf = pickle.load(savefile)
# --------------
plot_corner(data_wf, weights=weights_wf, addvcprior=False)
plt.savefig("figures/PDFs/corner_wf.pdf")
plt.close()
# --------------
# Which potential is preferred?
data_noforce, potindx, weights, evidences = read_mcmc(evi_func=evi_harmonic)
fig = plt.figure(figsize=(6, 4))
bovy_plot.bovy_plot(
potindx,
np.log(evidences),
"o",
xrange=[-1, 34],
yrange=[-35, -22],
xlabel=r"$\mathrm{Potential\ index}$",
ylabel=r"$\ln\ \mathrm{evidence}$",
)
data_noforce, potindx, weights, evidences = read_mcmc(evi_func=evi_laplace)
bovy_plot.bovy_plot(potindx, np.log(evidences) - 30.0, "d", overplot=True)
data_noforce, potindx, weights, evidences = read_mcmc(
evi_func=lambda x: np.exp(np.amax(x[:, -1]))
)
bovy_plot.bovy_plot(potindx, np.log(evidences) - 8.0, "s", overplot=True)
data_noforce, potindx, weights, evidences = read_mcmc(
evi_func=lambda x: np.exp(-25.0)
if (np.log(evi_harmonic(x)) > -25.0)
else np.exp(-50.0)
)
bovy_plot.bovy_plot(potindx, np.log(evidences), "o", overplot=True)
plt.savefig("figures/PDFs/preferred_pot.pdf")
plt.close()
###############################################################
# Look at the results for individual potentials
# --------------
# The flattening $c$
npot = 32
nwalkers = 12
plt.figure(figsize=(16, 6))
cmap = cm.plasma
maxl = np.zeros((npot, 2))
for en, ii in enumerate(range(npot)):
data_ip, _, weights_ip, evi_ip = read_mcmc(singlepot=ii, evi_func=evi_harmonic)
try:
maxl[en, 0] = np.amax(data_ip[:, -1])
maxl[en, 1] = np.log(evi_ip[0])
except ValueError:
maxl[en] = -10000000.0
plt.subplot(2, 4, en // 4 + 1)
bovy_plot.bovy_hist(
data_ip[:, 0],
range=[0.5, 2.0],
bins=26,
histtype="step",
color=cmap((en % 4) / 3.0),
normed=True,
xlabel=r"$c$",
lw=1.5,
overplot=True,
)
if en % 4 == 0:
bovy_plot.bovy_text(
r"$\mathrm{Potential\ %i\ to\ % i}$" % (en, en + 3),
size=17.0,
top_left=True,
)
plt.tight_layout()
plt.savefig("figures/flattening_c.pdf")
plt.close()
###############################################################
# What is the effective prior in $(F_R,F_Z)$?
frfzprior_savefilename = "frfzprior.pkl"
if not os.path.exists(frfzprior_savefilename):
# Compute for each potential separately
nvoc = 10000
ro = 8.0
npot = 32
fs = np.zeros((2, nvoc, npot))
for en, ii in tqdm(enumerate(range(npot))):
fn = f"output/fitsigma/mwpot14-fitsigma-{i:02}.dat"
# Read the potential parameters
with open(fn, "r") as savefile:
line1 = savefile.readline()
potparams = [float(s) for s in (line1.split(":")[1].split(","))]
for jj in range(nvoc):
c = np.random.uniform() * 1.5 + 0.5
tvo = np.random.uniform() * 50.0 + 200.0
pot = mw_pot.setup_potential(potparams, c, False, False, REFR0, tvo)
fs[:, jj, ii] = np.array(pal5_util.force_pal5(pot, 23.46, REFR0, tvo))[
:2
]
save_pickles(frfzprior_savefilename, fs)
else:
with open(frfzprior_savefilename, "rb") as savefile:
fs = pickle.load(savefile)
# --------------
plt.figure(figsize=(6, 6))
bovy_plot.scatterplot(
fs[0].flatten(),
fs[1].flatten(),
"k,",
xrange=[-1.75, -0.25],
yrange=[-2.5, -1.2],
xlabel=r"$F_R(\mathrm{Pal\ 5})$",
ylabel=r"$F_Z(\mathrm{Pal\ 5})$",
onedhists=True,
)
bovy_plot.scatterplot(
data_wf[:, 7],
data_wf[:, 8],
weights=weights_wf,
bins=26,
xrange=[-1.75, -0.25],
yrange=[-2.5, -1.2],
justcontours=True,
cntrcolors="w",
overplot=True,
onedhists=True,
)
plt.axvline(-0.81, color=sns.color_palette()[0])
plt.axhline(-1.85, color=sns.color_palette()[0])
plt.savefig("figures/effective_force_prior.pdf")
plt.close()
# --------------
# The ratio of the posterior and the prior
bovy_plot.bovy_print(
axes_labelsize=17.0,
text_fontsize=12.0,
xtick_labelsize=14.0,
ytick_labelsize=14.0,
)
plt.figure(figsize=(12.5, 4))
def axes_white():
for k, spine in plt.gca().spines.items(): # ax.spines is a dictionary
spine.set_color("w")
plt.gca().tick_params(axis="x", which="both", colors="w")
plt.gca().tick_params(axis="y", which="both", colors="w")
[t.set_color("k") for t in plt.gca().xaxis.get_ticklabels()]
[t.set_color("k") for t in plt.gca().yaxis.get_ticklabels()]
return None
bins = 32
trange = [[-1.75, -0.25], [-2.5, -1.2]]
tw = copy.deepcopy(weights_wf)
tw[index_wf == 14] = 0.0 # Didn't converge properly
H_prior, xedges, yedges = np.histogram2d(
fs[0].flatten(), fs[1].flatten(), bins=bins, range=trange, normed=True
)
H_post, xedges, yedges = np.histogram2d(
data_wf[:, 7], data_wf[:, 8], weights=tw, bins=bins, range=trange, normed=True,
)
H_like = H_post / H_prior
H_like[H_prior == 0.0] = 0.0
plt.subplot(1, 3, 1)
bovy_plot.bovy_dens2d(
H_prior.T,
origin="lower",
cmap="viridis",
interpolation="nearest",
xrange=[xedges[0], xedges[-1]],
yrange=[yedges[0], yedges[-1]],
xlabel=r"$F_R(\mathrm{Pal\ 5})\,(\mathrm{km\,s}^{-1}\,\mathrm{Myr}^{-1})$",
ylabel=r"$F_Z(\mathrm{Pal\ 5})\,(\mathrm{km\,s}^{-1}\,\mathrm{Myr}^{-1})$",
gcf=True,
)
bovy_plot.bovy_text(r"$\mathbf{Prior}$", top_left=True, size=19.0, color="w")
axes_white()
plt.subplot(1, 3, 2)
bovy_plot.bovy_dens2d(
H_post.T,
origin="lower",
cmap="viridis",
interpolation="nearest",
xrange=[xedges[0], xedges[-1]],
yrange=[yedges[0], yedges[-1]],
xlabel=r"$F_R(\mathrm{Pal\ 5})\,(\mathrm{km\,s}^{-1}\,\mathrm{Myr}^{-1})$",
gcf=True,
)
bovy_plot.bovy_text(r"$\mathbf{Posterior}$", top_left=True, size=19.0, color="w")
axes_white()
plt.subplot(1, 3, 3)
bovy_plot.bovy_dens2d(
H_like.T,
origin="lower",
cmap="viridis",
interpolation="nearest",
vmin=0.1,
vmax=4.0,
xrange=[xedges[0], xedges[-1]],
yrange=[yedges[0], yedges[-1]],
xlabel=r"$F_R(\mathrm{Pal\ 5})\,(\mathrm{km\,s}^{-1}\,\mathrm{Myr}^{-1})$",
gcf=True,
)
bovy_plot.bovy_text(r"$\mathbf{Likelihood}$", top_left=True, size=19.0, color="w")
axes_white()
def qline(FR, q=0.95):
return 2.0 * FR / q ** 2.0
q = 0.94
plt.plot([-1.25, -0.2], [qline(-1.25, q=q), qline(-0.2, q=q)], "w--")
bovy_plot.bovy_text(-1.7, -2.2, r"$q_\Phi = 0.94$", size=16.0, color="w")
plt.plot((-1.25, -1.02), (-2.19, qline(-1.02, q=q)), "w-", lw=0.8)
plt.tight_layout()
plt.savefig(
"figures/pal5post.pdf", bbox_inches="tight",
)
plt.close()
# --------------
# Projection onto the direction perpendicular to constant $q = 0.94$:
frs = np.tile(0.5 * (xedges[:-1] + xedges[1:]), (len(yedges) - 1, 1)).T
fzs = np.tile(0.5 * (yedges[:-1] + yedges[1:]), (len(xedges) - 1, 1))
plt.figure(figsize=(6, 4))
txlabel = r"$F_\perp$"
dum = bovy_plot.bovy_hist(
(-2.0 * (frs + 0.8) + 0.94 ** 2.0 * (fzs + 1.82)).flatten(),
weights=H_prior.flatten(),
bins=21,
histtype="step",
lw=2.0,
xrange=[-1.5, 1.5],
xlabel=txlabel,
normed=True,
)
dum = bovy_plot.bovy_hist(
(-2.0 * (frs + 0.8) + 0.94 ** 2.0 * (fzs + 1.82)).flatten(),
weights=H_post.flatten(),
bins=21,
histtype="step",
lw=2.0,
overplot=True,
xrange=[-1.5, 1.5],
normed=True,
)
dum = bovy_plot.bovy_hist(
(-2.0 * (frs + 0.8) + 0.94 ** 2.0 * (fzs + 1.82)).flatten(),
weights=H_like.flatten(),
bins=21,
histtype="step",
lw=2.0,
overplot=True,
xrange=[-1.5, 1.5],
normed=True,
)
plt.savefig("figures/PDFs/projection_perp_to_q0p94.pdf")
plt.close()
mq = (
np.sum(
(-2.0 * (frs + 0.8) + 0.94 ** 2.0 * (fzs + 1.82)).flatten()
* H_like.flatten()
)
) / np.sum(H_like.flatten())
print(
mq,
np.sqrt(
(
np.sum(
((-2.0 * (frs + 0.8) + 0.94 ** 2.0 * (fzs + 1.82)).flatten() - mq)
** 2.0
* H_like.flatten()
)
)
/ np.sum(H_like.flatten())
),
)
# --------------
# Projection onto the direction parallel to constant $q = 0.94$:
frs = np.tile(0.5 * (xedges[:-1] + xedges[1:]), (len(yedges) - 1, 1)).T
fzs = np.tile(0.5 * (yedges[:-1] + yedges[1:]), (len(xedges) - 1, 1))
plt.figure(figsize=(6, 4))
txlabel = r"$F_\parallel$"
dum = bovy_plot.bovy_hist(
(0.94 ** 2.0 * (frs + 0.8) + 2.0 * (fzs + 1.82)).flatten(),
weights=H_prior.flatten(),
bins=21,
histtype="step",
lw=2.0,
xrange=[-2.5, 2.5],
xlabel=txlabel,
normed=True,
)
dum = bovy_plot.bovy_hist(
(0.94 ** 2.0 * (frs + 0.8) + 2.0 * (fzs + 1.82)).flatten(),
weights=H_post.flatten(),
bins=21,
histtype="step",
lw=2.0,
overplot=True,
xrange=[-2.5, 2.5],
normed=True,
)
dum = bovy_plot.bovy_hist(
(0.94 ** 2.0 * (frs + 0.8) + 2.0 * (fzs + 1.82)).flatten(),
weights=H_like.flatten(),
bins=21,
histtype="step",
lw=2.0,
overplot=True,
xrange=[-2.5, 2.5],
normed=True,
)
plt.savefig("figures/PDFs/projection_prll_to_q0p94.pdf")
plt.close()
mq = (
np.sum(
(0.94 ** 2.0 * (frs + 0.8) + 2.0 * (fzs + 1.82)).flatten()
* H_like.flatten()
)
) / np.sum(H_like.flatten())
print(
mq,
np.sqrt(
(
np.sum(
((0.94 ** 2.0 * (frs + 0.8) + 2.0 * (fzs + 1.82)).flatten() - mq)
** 2.0
* H_like.flatten()
)
)
/ np.sum(H_like.flatten())
),
)
# --------------
# Thus, there is only a weak constraint on $F_\parallel$.
nrow = int(np.ceil(npot / 4.0))
plt.figure(figsize=(16, nrow * 4))
for en, ii in enumerate(range(npot)):
plt.subplot(nrow, 4, en + 1)
if ii % 4 == 0:
tylabel = r"$F_Z(\mathrm{Pal\ 5})$"
else:
tylabel = None
if ii // 4 == nrow - 1:
txlabel = r"$F_R(\mathrm{Pal\ 5})$"
else:
txlabel = None
bovy_plot.scatterplot(
fs[0][:, en],
fs[1][:, en],
"k,",
xrange=[-1.75, -0.25],
yrange=[-2.5, -1.0],
xlabel=txlabel,
ylabel=tylabel,
gcf=True,
)
bovy_plot.scatterplot(
data_wf[:, 7],
data_wf[:, 8],
weights=weights_wf,
bins=26,
xrange=[-1.75, -0.25],
yrange=[-2.5, -1.0],
justcontours=True,
cntrcolors="w",
overplot=True,
)
bovy_plot.scatterplot(
data_wf[index_wf == ii, 7],
data_wf[index_wf == ii, 8],
weights=weights_wf[index_wf == ii],
bins=26,
xrange=[-1.75, -0.25],
yrange=[-2.5, -1.0],
justcontours=True,
# cntrcolors=sns.color_palette()[2],
overplot=True,
)
plt.axvline(-0.80, color=sns.color_palette()[0])
plt.axhline(-1.83, color=sns.color_palette()[0])
bovy_plot.bovy_text(r"$\mathrm{Potential}\ %i$" % ii, size=17.0, top_left=True)
plt.savefig("figures/PDFs/constain_F_prll.pdf")
plt.close()
# --------------
# Let's plot four representative ones for the paper:
bovy_plot.bovy_print(
axes_labelsize=17.0,
text_fontsize=12.0,
xtick_labelsize=14.0,
ytick_labelsize=14.0,
)
nullfmt = NullFormatter()
nrow = 1
plt.figure(figsize=(15, nrow * 4))
for en, ii in enumerate([0, 15, 24, 25]):
plt.subplot(nrow, 4, en + 1)
if en % 4 == 0:
tylabel = (
r"$F_Z(\mathrm{Pal\ 5})\,(\mathrm{km\,s}^{-1}\,\mathrm{Myr}^{-1})$"
)
else:
tylabel = None
if en // 4 == nrow - 1:
txlabel = (
r"$F_R(\mathrm{Pal\ 5})\,(\mathrm{km\,s}^{-1}\,\mathrm{Myr}^{-1})$"
)
else:
txlabel = None
bovy_plot.scatterplot(
fs[0][:, ii],
fs[1][:, ii],
"k,",
bins=31,
xrange=[-1.75, -0.25],
yrange=[-2.5, -1.0],
xlabel=txlabel,
ylabel=tylabel,
gcf=True,
)
bovy_plot.scatterplot(
data_wf[:, 7],
data_wf[:, 8],
weights=weights_wf,
bins=21,
xrange=[-1.75, -0.25],
yrange=[-2.5, -1.0],
justcontours=True,
cntrcolors=sns.color_palette("colorblind")[2],
cntrls="--",
cntrlw=2.0,
overplot=True,
)
bovy_plot.scatterplot(
data_wf[index_wf == ii, 7],
data_wf[index_wf == ii, 8],
weights=weights_wf[index_wf == ii],
bins=21,
xrange=[-1.75, -0.25],
yrange=[-2.5, -1.0],
justcontours=True,
cntrcolors=sns.color_palette("colorblind")[0],
cntrlw=2.5,
overplot=True,
)
if en > 0:
plt.gca().yaxis.set_major_formatter(nullfmt)
plt.tight_layout()
plt.savefig(
"figures/pal5post_examples.pdf", bbox_inches="tight",
)
plt.close()
###############################################################
# What about $q_\Phi$?
bins = 47
plt.figure(figsize=(6, 4))
dum = bovy_plot.bovy_hist(
np.sqrt(2.0 * fs[0].flatten() / fs[1].flatten()),
histtype="step",
lw=2.0,
bins=bins,
xlabel=r"$q_\mathrm{\Phi}$",
xrange=[0.7, 1.25],
normed=True,
)
dum = bovy_plot.bovy_hist(
np.sqrt(16.8 / 8.4 * data_wf[:, -2] / data_wf[:, -1]),
weights=weights_wf,
histtype="step",
lw=2.0,
bins=bins,
overplot=True,
xrange=[0.7, 1.25],
normed=True,
)
mq = np.sum(
np.sqrt(16.8 / 8.4 * data_wf[:, -2] / data_wf[:, -1]) * weights_wf
) / np.sum(weights_wf)
sq = np.sqrt(
np.sum(
(np.sqrt(16.8 / 8.4 * data_wf[:, -2] / data_wf[:, -1]) - mq) ** 2.0
* weights_wf
)
/ np.sum(weights_wf)
)
print("From posterior samples: q = %.3f +/- %.3f" % (mq, sq))
Hq_post, xedges = np.histogram(
np.sqrt(16.8 / 8.4 * data_wf[:, -2] / data_wf[:, -1]),
weights=weights_wf,
bins=bins,
range=[0.7, 1.25],
normed=True,
)
Hq_prior, xedges = np.histogram(
np.sqrt(2.0 * fs[0].flatten() / fs[1].flatten()),
bins=bins,
range=[0.7, 1.25],
normed=True,
)
qs = 0.5 * (xedges[:-1] + xedges[1:])
Hq_like = Hq_post / Hq_prior
Hq_like[Hq_post == 0.0] = 0.0
mq = np.sum(qs * Hq_like) / np.sum(Hq_like)
sq = np.sqrt(np.sum((qs - mq) ** 2.0 * Hq_like) / np.sum(Hq_like))
print("From likelihood of samples: q = %.3f +/- %.3f" % (mq, sq))
plt.savefig("figures/q_phi.pdf")
plt.close()
# It appears that $q_\Phi$ is the quantity that is the most strongly constrained by the Pal 5 data.
###############################################################
# A sampling of tracks from the MCMC
savefilename = "mwpot14-pal5-mcmcTracks.pkl"
pmdecpar = 2.257 / 2.296
pmdecperp = -2.296 / 2.257
if os.path.exists(savefilename):
with open(savefilename, "rb") as savefile:
pal5_track_samples = pickle.load(savefile)
forces = pickle.load(savefile)
all_potparams = pickle.load(savefile)
all_params = pickle.load(savefile)
else:
np.random.seed(1)
ntracks = 21
multi = 8
pal5_track_samples = np.zeros((ntracks, 2, 6, pal5varyc[0].shape[1]))
forces = np.zeros((ntracks, 2))
all_potparams = np.zeros((ntracks, 5))
all_params = np.zeros((ntracks, 7))
for ii in range(ntracks):
# Pick a random potential from among the set, but leave 14 out
pindx = 14
while pindx == 14:
pindx = np.random.permutation(32)[0]
# Load this potential
fn = f"output/fitsigma/mwpot14-fitsigma-{pindx:02}.dat"
with open(fn, "r") as savefile:
line1 = savefile.readline()
potparams = [float(s) for s in (line1.split(":")[1].split(","))]
all_potparams[ii] = potparams
# Now pick a random sample from this MCMC chain
tnburn = mcmc_util.determine_nburn(fn)
tdata = np.loadtxt(fn, comments="#", delimiter=",")
tdata = tdata[tnburn::]
tdata = tdata[np.random.permutation(len(tdata))[0]]
all_params[ii] = tdata
tvo = tdata[1] * REFV0
pot = mw_pot.setup_potential(potparams, tdata[0], False, False, REFR0, tvo)
forces[ii, :] = pal5_util.force_pal5(pot, 23.46, REFR0, tvo)[:2]
# Now compute the stream model for this setup
dist = tdata[2] * 22.0
pmra = -2.296 + tdata[3] + tdata[4]
pmdecpar = 2.257 / 2.296
pmdecperp = -2.296 / 2.257
pmdec = -2.257 + tdata[3] * pmdecpar + tdata[4] * pmdecperp
vlos = -58.7
sigv = 0.4 * np.exp(tdata[5])
prog = Orbit(
[229.018, -0.124, dist, pmra, pmdec, vlos],
radec=True,
ro=REFR0,
vo=tvo,
solarmotion=[-11.1, 24.0, 7.25],
)
tsdf_trailing, tsdf_leading = pal5_util.setup_sdf(
pot,
prog,
sigv,
10.0,
REFR0,
tvo,
multi=multi,
nTrackChunks=8,
trailing_only=False,
verbose=True,
useTM=False,
)
# Compute the track
for jj, sdf in enumerate([tsdf_trailing, tsdf_leading]):
trackRADec = bovy_coords.lb_to_radec(
sdf._interpolatedObsTrackLB[:, 0],
sdf._interpolatedObsTrackLB[:, 1],
degree=True,
)
trackpmRADec = bovy_coords.pmllpmbb_to_pmrapmdec(
sdf._interpolatedObsTrackLB[:, 4],
sdf._interpolatedObsTrackLB[:, 5],
sdf._interpolatedObsTrackLB[:, 0],
sdf._interpolatedObsTrackLB[:, 1],
degree=True,
)
# Store the track
pal5_track_samples[ii, jj, 0] = trackRADec[:, 0]
pal5_track_samples[ii, jj, 1] = trackRADec[:, 1]
pal5_track_samples[ii, jj, 2] = sdf._interpolatedObsTrackLB[:, 2]
pal5_track_samples[ii, jj, 3] = sdf._interpolatedObsTrackLB[:, 3]
pal5_track_samples[ii, jj, 4] = trackpmRADec[:, 0]
pal5_track_samples[ii, jj, 5] = trackpmRADec[:, 1]
save_pickles(
savefilename, pal5_track_samples, forces, all_potparams, all_params
)
# --------------
bovy_plot.bovy_print(
axes_labelsize=17.0,
text_fontsize=12.0,
xtick_labelsize=14.0,
ytick_labelsize=14.0,
)
plt.figure(figsize=(12, 8))
gs = gridspec.GridSpec(2, 2, wspace=0.225, hspace=0.1, right=0.94)
ntracks = pal5_track_samples.shape[0]
cmap = cm.plasma
alpha = 0.7
for ii in range(ntracks):
tc = cmap((forces[ii, 1] + 2.5) / 1.0)
for jj in range(2):
# RA, Dec
plt.subplot(gs[0])
plt.plot(
pal5_track_samples[ii, jj, 0],
pal5_track_samples[ii, jj, 1],
"-",
color=tc,
alpha=alpha,
)
# RA, Vlos
plt.subplot(gs[1])
plt.plot(
pal5_track_samples[ii, jj, 0],
pal5_track_samples[ii, jj, 3],
"-",
color=tc,
alpha=alpha,
)
# RA, Dist
plt.subplot(gs[2])
plt.plot(
pal5_track_samples[ii, jj, 0],
pal5_track_samples[ii, jj, 2] - all_params[ii, 2] * 22.0,
"-",
color=tc,
alpha=alpha,
)
# RA, pm_parallel
plt.subplot(gs[3])
plt.plot(
pal5_track_samples[ii, jj, 0, : 500 + 500 * (1 - jj)],
np.sqrt(1.0 + (2.257 / 2.296) ** 2.0)
* (
(pal5_track_samples[ii, jj, 4, : 500 + 500 * (1 - jj)] + 2.296)
* pmdecperp
- (pal5_track_samples[ii, jj, 5, : 500 + 500 * (1 - jj)] + 2.257)
)
/ (pmdecpar - pmdecperp),
"-",
color=tc,
alpha=alpha,
)
plot_data_add_labels(
p1=(gs[0],), p2=(gs[1],), noxlabel_dec=True, noxlabel_vlos=True
)
plt.subplot(gs[2])
plt.xlim(250.0, 221.0)
plt.ylim(-3.0, 1.5)
bovy_plot._add_ticks()
plt.xlabel(r"$\mathrm{RA}\,(\mathrm{degree})$")
plt.ylabel(r"$\Delta\mathrm{Distance}\,(\mathrm{kpc})$")
plt.subplot(gs[3])
plt.xlim(250.0, 221.0)
plt.ylim(-0.5, 0.5)
bovy_plot._add_ticks()
plt.xlabel(r"$\mathrm{RA}\,(\mathrm{degree})$")
plt.ylabel(r"$\Delta\mu_\parallel\,(\mathrm{mas\,yr}^{-1})$")
# Colorbar
gs2 = gridspec.GridSpec(2, 1, wspace=0.0, left=0.95, right=0.975)
plt.subplot(gs2[:, -1])
sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=-2.5, vmax=-1.5))
sm._A = []
CB1 = plt.colorbar(sm, orientation="vertical", cax=plt.gca())
CB1.set_label(
r"$F_Z(\mathrm{Pal\ 5})\,(\mathrm{km\,s}^{-1}\,\mathrm{Myr}^{-1})$",
fontsize=18.0,
)
plt.savefig(
"figures/pal5tracksamples.pdf", bbox_inches="tight",
)
plt.close()
###############################################################
# What about Kuepper et al. (2015)?
# Can we recover the Kuepper et al. (2015) result as prior + $q_\Phi =
# 0.94 \pm 0.05$ + $V_c(R_0)$ between 200 and 280 km/s? For simplicity
# we will not vary $R_0$, which should not have a big impact.
s = sample_kuepper_flattening_post(50000, 0.94, 0.05)
plot_kuepper_samples(s)
plt.savefig("figures/kuepper_samples.pdf")
plt.close()
# The constraint on the potential flattening gets you far, but there
# is more going on (the constraint on the halo mass and scale
# parameter appear to come completely from the $V_c(R_0)$ constraint).
# Let's add the weak constraint on the sum of the forces, scaled to
# Kuepper et al.'s best-fit acceleration (which is higher than ours):
s = sample_kuepper_flatteningforce_post(50000, 0.94, 0.05, -0.83, 0.36)
plot_kuepper_samples(s)
plt.savefig("figures/kuepper_samples_flatF.pdf")
plt.close()
# This gets a tight relation between $M_\mathrm{halo}$ and the scale
# parameter of the halo, but does not lead to a constraint on either
# independently; the halo potential flattening constraint is that of
# Kuepper et al. Based on this, it appears that the information that
# ties down $M_\mathrm{halo}$ comes from the overdensities, which may
# be spurious (Thomas et al. 2016) and whose use in dynamical modeling
# is dubious anyway.
# What is Kuepper et al.'s prior on $a_{\mathrm{Pal\ 5}}$?
apal5s = []
ns = 100000
for ii in range(ns):
Mh = np.random.uniform() * (10.0 - 0.001) + 0.001
a = np.random.uniform() * (100.0 - 0.1) + 0.1
q = np.random.uniform() * (1.8 - 0.2) + 0.2
pot = setup_potential_kuepper(Mh, a)
FR, FZ = force_pal5_kuepper(pot, q)
apal5s.append(np.sqrt(FR ** 2.0 + FZ ** 2.0))
apal5s = np.array(apal5s)
plt.figure(figsize=(6, 4))
dum = bovy_plot.bovy_hist(
apal5s, range=[0.0, 10.0], lw=2.0, bins=51, histtype="step", normed=True,
)
plt.savefig("figures/kuepper_samples_prior.pdf")
plt.close()
def main(args: Optional[list] = None,
opts: Optional[argparse.Namespace] = None):
"""Fit MWPotential2014 Script Function.
Parameters
----------
args : list, optional
an optional single argument that holds the sys.argv list,
except for the script name (e.g., argv[1:])
"""
if opts is not None and args is None:
pass
else:
parser = make_parser()
opts = parser.parse_args(args)
fpath = opts.fpath + "/" if not opts.fpath.endswith("/") else opts.fpath
opath = opts.opath + "/" if not opts.opath.endswith("/") else opts.opath
if not os.path.exists(opts.opath):
os.makedirs(opts.opath)
if not os.path.exists(opts.fpath):
os.makedirs(opts.fpath)
# ----------------------------------------------------------
# Basic, Bovy (2015) fit with $c=1$
# -----------------------
# Using the Clemens CO terminal-velocity data:
plt.figure(figsize=(16, 5))
p_b15 = mw_pot.fit(fitc=False, c=1.0, plots=fpath + "Clemens-c_1.pdf")
plt.close()
# -----------------------
# Using the McClure-Griffiths & Dickey HI terminal-velocity data instead
plt.figure(figsize=(16, 5))
p_b15_mc16 = mw_pot.fit(fitc=False,
c=1.0,
mc16=True,
plots=fpath + "McClure-c_1.pdf")
plt.close()
# We ran the initial analysis with Clemens, which we keep here, until we
# start interpreting the Pal 5 and GD-1 data; then we switch to the
# McClure-Griffths & Dickey data. The resulting best-fit parameters here
# are almost the same, well within each others errors.
# ----------------------------------------------------------
# Fits with $c \neq 1$
# -----------------------
plt.figure(figsize=(16, 5))
p_b15_cp5 = mw_pot.fit(fitc=False,
c=0.5,
plots=fpath + "Clemens-c_0p5.pdf")
plt.close()
# -----------------------
plt.figure(figsize=(16, 5))
p_b15_c1p5 = mw_pot.fit(fitc=False,
c=1.5,
plots=fpath + "Clemens-c_1p5.pdf")
# All look pretty similar...
# -----------------------
bf_savefilename = opath + "mwpot14varyc-bf.pkl"
if os.path.exists(bf_savefilename):
with open(bf_savefilename, "rb") as savefile:
cs = pickle.load(savefile)
bf_params = pickle.load(savefile)
else:
cs = np.arange(0.5, 4.1, 0.1)
bf_params = []
for c in tqdm(cs):
dum = mw_pot.fit(
fitc=False,
c=c,
plots=fpath + "mwpot14varyc-bf-fit.pdf",
)
bf_params.append(dum[0])
save_pickles(bf_savefilename, cs, bf_params)
# ----------------------------------------------------------
# Fits with free $c$
plt.figure(figsize=(16, 5))
p_b15_cfree = mw_pot.fit(fitc=True,
c=None,
plots=fpath + "Clemens-c_free.pdf")
# -----------------------
samples_savefilename = opath + "mwpot14varyc-samples.pkl"
if os.path.exists(samples_savefilename):
with open(samples_savefilename, "rb") as savefile:
s = pickle.load(savefile)
else:
s = mw_pot.sample(
nsamples=100000,
params=p_b15_cfree[0],
fitc=True,
c=None,
plots=fpath + "mwpot14varyc-samples.pdf",
_use_emcee=True,
)
save_pickles(samples_savefilename, s)
# -----------------------
plt.figure()
mw_pot.plot_samples(s,
True,
False,
savefig=fpath + "varyc-samples-corner.pdf")
# -----------------------
bovy_plot.bovy_print(
axes_labelsize=17.0,
text_fontsize=12.0,
xtick_labelsize=15.0,
ytick_labelsize=15.0,
)
su.plot_mcmc_c(
s,
False,
cs,
bf_params,
savefig=fpath + "varyc-samples-dependence.pdf",
)
# -----------------------
plt.figure(figsize=(4, 4))
dum = bovy_plot.bovy_hist(
s[7],
bins=36,
histtype="step",
lw=2.0,
xlabel=r"$c/a$",
xrange=[0.0, 4.0],
normed=True,
)
sortedc = np.array(sorted(s[7]))
plt.title("2.5%% and 0.5%% lower limits: %.2f, %.2f" % (
sortedc[int(np.floor(0.025 * len(sortedc)))],
sortedc[int(np.floor(0.005 * len(sortedc)))],
))
plt.savefig(fpath + "varyc-samples-shape_hist.pdf")
# -------------------------------------------
# Also fitting $R_0$ and $V_c(R_0)$
plt.figure(figsize=(16, 5))
p_b15_voro = mw_pot.fit(
fitc=True,
c=None,
fitvoro=True,
plots=fpath + "varyc-fitvoro-samples.pdf",
)
# -----------------------
samples_savefilename = opath + "mwpot14varyc-fitvoro-samples.pkl"
if os.path.exists(samples_savefilename):
with open(samples_savefilename, "rb") as savefile:
s = pickle.load(savefile)
else:
s = mw_pot.sample(
nsamples=100000,
params=p_b15_voro[0],
fitc=True,
c=None,
plots=fpath + "mwpot14varyc-fitvoro-samples.pdf",
fitvoro=True,
)
save_pickles(samples_savefilename, s)
# -----------------------
plt.figure()
mw_pot.plot_samples(
s,
True,
True,
savefig=fpath + "varyc-fitvoro-samples-corner.pdf",
)
# -----------------------
plt.figure()
bovy_plot.bovy_print(
axes_labelsize=17.0,
text_fontsize=12.0,
xtick_labelsize=15.0,
ytick_labelsize=15.0,
)
su.plot_mcmc_c(
s,
True,
cs,
bf_params,
add_families=True,
savefig=fpath + "varyc-fitvoro-samples-dependence.pdf",
)
if save_figures:
plt.savefig(
os.path.join(
os.getenv("PAPERSDIR"),
"2016-mwhalo-shape",
"mwpot14-varyc.pdf",
),
bbox_inches="tight",
)
# -----------------------
plt.figure(figsize=(4, 4))
dum = bovy_plot.bovy_hist(
s[9],
bins=36,
histtype="step",
lw=2.0,
xlabel=r"$c/a$",
xrange=[0.0, 4.0],
normed=True,
)
sortedc = np.array(sorted(s[9]))
plt.title("2.5%% and 0.5%% lower limits: %.2f, %.2f" % (
sortedc[int(np.floor(0.025 * len(sortedc)))],
sortedc[int(np.floor(0.005 * len(sortedc)))],
))
plt.savefig(fpath + "varyc-fitvoro-samples-shape_hist.pdf")
return
