def plotFidDF(options,args):
#Setup potential
params= numpy.array([-1.33663190049,0.998420232634,-3.49031638164,0.31949840593,-1.63965169376])
try:
pot= setup_potential(params,options,0)#Assume that the potential parameters come from a file with a single set of df parameters first
except RuntimeError: #if this set of parameters gives a nonsense potential
raise
ro= 1.
vo= params[1]
options.aAmethod='adiabatic'
aA= setup_aA(pot,options)
options.aAmethod='staeckel'
aAS= setup_aA(pot,options)
#Setup DF
qdf= quasiisothermaldf(2./8.,0.25,0.3,1.,0.875,aA=aAS,pot=pot,cutcounter=True)
qdfa= quasiisothermaldf(2./8.,0.25,0.3,1.,0.875,aA=aA,pot=pot,cutcounter=True)
if options.type.lower() == 'lzjr':
njs= 201
jrs= numpy.linspace(0.,500.,njs)/ro/vo/_REFR0/_REFV0
lzs= numpy.linspace(0.0,3600.,njs)/ro/vo/_REFR0/_REFV0
plotthis= qdf((numpy.tile(jrs,(njs,1)).flatten(),
numpy.tile(lzs,(njs,1)).T.flatten(),
numpy.zeros((njs,njs)).flatten())).reshape((njs,njs))
bovy_plot.bovy_print()
bovy_plot.bovy_dens2d(plotthis.T,origin='lower',cmap='gist_yarg',
xlabel=r'$L_z\ (220\,\mathrm{km\,s}^{-1}\,\mathrm{kpc})$',
ylabel=r'$J_R\ (220\,\mathrm{km\,s}^{-1}\,\mathrm{kpc})$',
xrange=[0.,3600./220.],
yrange=[0.,500./220.],
onedhists=True,
interpolation='nearest',
cntrmass=True,contours=True,
levels= special.erf(0.5*numpy.arange(1,4)))
elif options.type.lower() == 'jrjz':
njs= 201
jrs= numpy.linspace(0.,500.,njs)/ro/vo/_REFR0/_REFV0
jzs= numpy.linspace(0.,250.,njs)/ro/vo/_REFR0/_REFV0
plotthis= qdf((numpy.tile(jrs,(njs,1)).T.flatten(),
0.9*numpy.ones((njs,njs)).flatten(),
numpy.tile(jzs,(njs,1)).flatten())).reshape((njs,njs))
bovy_plot.bovy_print()
bovy_plot.bovy_dens2d(plotthis.T,origin='lower',cmap='gist_yarg',
xlabel=r'$J_R\ (220\,\mathrm{km\,s}^{-1}\,\mathrm{kpc})$',
ylabel=r'$J_Z\ (220\,\mathrm{km\,s}^{-1}\,\mathrm{kpc})$',
xrange=[0.,500./220.],
yrange=[0.,250./220.],
onedhists=True,
interpolation='nearest',
cntrmass=True,contours=True,
levels= special.erf(0.5*numpy.arange(1,4)))
elif options.type.lower() == 'tilt':
zs= numpy.linspace(0.,5.,101)
tilt= numpy.array([qdf.tilt(1.,z/ro/_REFR0,gl=True) for z in zs])
bovy_plot.bovy_print(text_fontsize=20.,
legend_fontsize=24.,
xtick_labelsize=18.,
ytick_labelsize=18.,
axes_labelsize=24.)
line1= bovy_plot.bovy_plot(zs,tilt,'k-',
xlabel=r'$Z\ (\mathrm{kpc})$',
ylabel=r'$\mathrm{tilt\ of\ the\ velocity\ ellipsoid}\ (\mathrm{deg})$',
xrange=[0.,5.],
yrange=[-5.,30.])
line2= bovy_plot.bovy_plot(zs,zs*0.,'k--',overplot=True)
pyplot.legend((line1[0],line2[0]),
(r'$\mathrm{St\ddot{a}ckel\ actions}$',
r'$\mathrm{Adiabatic\ actions}$'),
loc='upper left',#bbox_to_anchor=(.91,.375),
numpoints=2,
prop={'size':18},
frameon=False)
bovy_plot.bovy_plot(zs,numpy.arctan(zs/ro/_REFR0)/numpy.pi*180.,
'-',color='0.65',
overplot=True)
pyplot.errorbar(numpy.array([1.]),
numpy.array([7.3]),
yerr=numpy.array([1.8,1.8]).reshape((2,1)),
color='k',fmt='o',ms=8)
bovy_plot.bovy_text(.55,8.2,r'$\mathrm{S08}$',fontsize=14.)
elif options.type.lower() == 'sigz':
zs= numpy.linspace(0.,5.,101)
sigz2= numpy.array([qdf.sigmaz2(1.,z/ro/_REFR0,gl=True) for z in zs])
sigz2a= numpy.array([qdfa.sigmaz2(1.,z/ro/_REFR0,gl=True) for z in zs])
bovy_plot.bovy_print(text_fontsize=20.,
legend_fontsize=24.,
xtick_labelsize=18.,
ytick_labelsize=18.,
axes_labelsize=24.)
line1= bovy_plot.bovy_plot(zs,numpy.sqrt(sigz2)*vo*_REFV0,'k-',
xlabel=r'$Z\ (\mathrm{kpc})$',
ylabel=r'$\sigma_Z(Z)\ (\mathrm{km\,s}^{-1})$',
xrange=[0.,5.],
yrange=[0.,60.])
line2= bovy_plot.bovy_plot(zs,numpy.sqrt(sigz2a)*vo*_REFV0,
'k--',overplot=True)
#pyplot.legend((line1[0],line2[0]),
# (r'$\mathrm{St\ddot{a}ckel\ actions}$',
# r'$\mathrm{Adiabatic\ actions}$'),
# loc='lower left',#bbox_to_anchor=(.91,.375),
# numpoints=2,
# prop={'size':16},
# frameon=False)
elif options.type.lower() == 'densz':
zs= numpy.linspace(0.,5.,101)
densz= numpy.array([qdf.density(1.,z/ro/_REFR0,gl=True) for z in zs])
densza= numpy.array([qdfa.density(1.,z/ro/_REFR0,gl=True) for z in zs])
bovy_plot.bovy_print(text_fontsize=20.,
legend_fontsize=24.,
xtick_labelsize=18.,
ytick_labelsize=18.,
axes_labelsize=24.)
line1= bovy_plot.bovy_plot(zs,densz/densz[0],'k-',
xlabel=r'$Z\ (\mathrm{kpc})$',
ylabel=r'$\nu_*(R_0,Z)/\nu_*(R_0,0)$',
xrange=[0.,5.],
semilogy=True)
line2= bovy_plot.bovy_plot(zs,densza/densza[0],'k--',overplot=True)
pyplot.legend((line1[0],line2[0]),
(r'$\mathrm{St\ddot{a}ckel\ actions}$',
r'$\mathrm{Adiabatic\ actions}$'),
loc='upper right',#bbox_to_anchor=(.91,.375),
numpoints=2,
prop={'size':18},
frameon=False)
#Create inset with profile at different R
denszr12= numpy.array([qdf.density(11./8.,z/ro/_REFR0,gl=True) for z in zs])
denszr4= numpy.array([qdf.density(5./8.,z/ro/_REFR0,gl=True) for z in zs])
insetAxes= pyplot.axes([0.15,0.12,0.4,0.4])
line1= insetAxes.semilogy(zs,densz/densz[0],'k-')
line2= insetAxes.semilogy(zs,denszr12/denszr12[0],'k:')
line3= insetAxes.semilogy(zs,denszr4/denszr4[0],'k--')
nullfmt = NullFormatter() # no labels
insetAxes.xaxis.set_major_formatter(nullfmt)
insetAxes.yaxis.set_major_formatter(nullfmt)
pyplot.legend((line3[0],line1[0],line2[0]),
(r'$R = 5\,\mathrm{kpc}$',
r'$R = 8\,\mathrm{kpc}$',
r'$R = 11\,\mathrm{kpc}$'),
loc='lower left',#bbox_to_anchor=(.91,.375),
numpoints=2,
prop={'size':12},
frameon=False)
elif options.type.lower() == 'densr':
rs= numpy.linspace(4.,15.,101)
densr= numpy.array([qdf.density(r/ro/_REFR0,1./ro/_REFR0,gl=True) for r in rs])
densra= numpy.array([qdfa.density(r/ro/_REFR0,1./ro/_REFR0,gl=True) for r in rs])
bovy_plot.bovy_print(text_fontsize=20.,
legend_fontsize=24.,
xtick_labelsize=18.,
ytick_labelsize=18.,
axes_labelsize=24.)
line1= bovy_plot.bovy_plot(rs,densr/densr[numpy.argmin((rs-8.)**2.)],'k-',
xlabel=r'$R\ (\mathrm{kpc})$',
ylabel=r'$\nu_*(R,1\,\mathrm{kpc})/\nu_*(R_0,1\,\mathrm{kpc})$',
xrange=[4.,15.],
semilogy=True)
line2= bovy_plot.bovy_plot(rs,densra/densra[numpy.argmin((rs-8.)**2.)],'k--',overplot=True)
line3= bovy_plot.bovy_plot(rs,numpy.exp(-(rs-8.)/2.),'-',
overplot=True,
color='0.65')
#pyplot.legend((line1[0],line2[0]),
# (r'$\mathrm{St\ddot{a}ckel\ actions}$',
# r'$\mathrm{Adiabatic\ actions}$'),
# loc='lower left',#bbox_to_anchor=(.91,.375),
# numpoints=2,
# prop={'size':16},
# frameon=False)
bovy_plot.bovy_end_print(options.outfilename)
def plot_hzszq(options,args):
"""Plot sz,hz, q"""
if len(args) == 0.:
print "Must provide a savefilename ..."
print "Returning ..."
return None
nqs, nszs, nhzs= 31, 51, 51
#nqs, nszs, nhzs= 5,5,5
if os.path.exists(args[0]):
#Load
savefile= open(args[0],'rb')
hzs= pickle.load(savefile)
szs= pickle.load(savefile)
qs= pickle.load(savefile)
savefile.close()
else:
qs= numpy.linspace(0.5,1.,nqs)
szs= numpy.linspace(15.,50.,nszs)
hzs= numpy.zeros((nqs,nszs))
for ii in range(nqs):
print "Working on potential %i / %i ..." % (ii+1,nqs)
#Setup potential
lp= LogarithmicHaloPotential(normalize=1.,q=qs[ii])
if options.aAmethod.lower() == 'staeckel':
aA= actionAngleStaeckel(pot=lp,delta=0.45,c=True)
else:
aA=actionAngleAdiabaticGrid(pot=pot,nR=16,nEz=16,nEr=31,nLz=31,
zmax=1.,Rmax=5.)
for jj in range(nszs):
qdf= quasiisothermaldf(options.hr/8.,2.*szs[jj]/220.,
szs[jj]/220.,7./8.,7./8.,pot=lp,
aA=aA,cutcounter=True)
hzs[ii,jj]= qdf.estimate_hz(1.,z=0.125)
#Save
save_pickles(args[0],hzs,szs,qs)
#Re-sample
hzsgrid= numpy.linspace(50.,1500.,nhzs)/8000.
qs2d= numpy.zeros((nhzs,nszs))
for ii in range(nszs):
interpQ= interpolate.UnivariateSpline(hzs[:,ii],qs,k=3)
qs2d[:,ii]= interpQ(hzsgrid)
qs2d[(hzsgrid < hzs[0,ii]),ii]= numpy.nan
qs2d[(hzsgrid > hzs[-1,ii]),ii]= numpy.nan
#Now plot
bovy_plot.bovy_print(fig_width=6.,
text_fontsize=20.,
legend_fontsize=24.,
xtick_labelsize=18.,
ytick_labelsize=18.,
axes_labelsize=24.)
bovy_plot.bovy_dens2d(qs2d.T,origin='lower',cmap='jet',
interpolation='gaussian',
# interpolation='nearest',
ylabel=r'$\sigma_z\ [\mathrm{km\,s}^{-1}]$',
xlabel=r'$h_z\ [\mathrm{pc}]$',
zlabel=r'$\mathrm{flattening}\ q$',
yrange=[szs[0],szs[-1]],
xrange=[8000.*hzsgrid[0],8000.*hzsgrid[-1]],
# vmin=0.5,vmax=1.,
contours=False,
colorbar=True,shrink=0.78)
_OVERPLOTMAPS= True
if _OVERPLOTMAPS:
fehs= monoAbundanceMW.fehs()
afes= monoAbundanceMW.afes()
npops= len(fehs)
mapszs= []
maphzs= []
for ii in range(npops):
thissz, thiserr= monoAbundanceMW.sigmaz(fehs[ii],afes[ii],err=True)
if thiserr/thissz > 0.1:
continue
thishz, thiserr= monoAbundanceMW.hz(fehs[ii],afes[ii],err=True)
if thiserr/thishz > 0.1:
continue
mapszs.append(thissz)
maphzs.append(thishz)
mapszs= numpy.array(mapszs)
maphzs= numpy.array(maphzs)
bovy_plot.bovy_plot(maphzs,mapszs,'ko',overplot=True,mfc='none',mew=1.5)
bovy_plot.bovy_text(r'$h_R = %i\,\mathrm{kpc}$' % int(options.hr),
bottom_right=True)
bovy_plot.bovy_end_print(options.plotfilename)
def plot_hrhrvshr(options,args):
"""Plot hr^out/hr^in as a function of hr for various sr"""
if len(args) == 0.:
print "Must provide a savefilename ..."
print "Returning ..."
return None
if os.path.exists(args[0]):
#Load
savefile= open(args[0],'rb')
plotthis= pickle.load(savefile)
hrs= pickle.load(savefile)
srs= pickle.load(savefile)
savefile.close()
else:
#Grid of models to test
hrs= numpy.linspace(options.hrmin,options.hrmax,options.nhr)
srs= numpy.linspace(options.srmin,options.srmax,options.nsr)
#Tile
hrs= numpy.tile(hrs,(options.nsr,1)).T
srs= numpy.tile(srs,(options.nhr,1))
plotthis= numpy.zeros((options.nhr,options.nsr))
#Setup potential and aA
poptions= setup_options(None)
#poptions.potential= 'dpdiskplhalofixbulgeflatwgasalt'
#params= [0.,0.,0.,0.,0.,0.,-1.16315,1.,-3.,0.4,0.]
poptions.potential= 'btii'
params= None
#pot= MWPotential
pot= setup_potential(params,poptions,1)
if options.aAmethod.lower() == 'staeckel':
aA= actionAngleStaeckel(pot=pot,delta=0.45,c=True)
else:
aA=actionAngleAdiabaticGrid(pot=pot,nR=16,nEz=16,nEr=31,nLz=31,
zmax=1.,Rmax=5.)
for ii in range(options.nhr):
for jj in range(options.nsr):
qdf= quasiisothermaldf(hrs[ii,jj]/8.,srs[ii,jj]/220.,
srs[ii,jj]/numpy.sqrt(3.)/220.,
7./8.,1.,
pot=pot,aA=aA)
plotthis[ii,jj]= qdf.estimate_hr(1.,z=0.8/8.,
dR=0.33,
gl=True)/hrs[ii,jj]*8.
print ii*options.nsr+jj+1, options.nsr*options.nhr, \
hrs[ii,jj], srs[ii,jj], plotthis[ii,jj]
#Save
save_pickles(args[0],plotthis,hrs,srs)
#Now plot
bovy_plot.bovy_print(fig_width=6.,
text_fontsize=20.,
legend_fontsize=24.,
xtick_labelsize=18.,
ytick_labelsize=18.,
axes_labelsize=24.)
indx= 0
lines= []
colors= [cm.jet(ii/float(options.nsr-1.)*1.+0.) for ii in range(options.nsr)]
lss= ['-' for ii in range(options.nsr)]#,'--','-.','..']
labels= []
lines.append(bovy_plot.bovy_plot(hrs[:,indx],plotthis[:,indx],
color=colors[indx],ls=lss[indx],
xrange=[0.5,5.5],
yrange=[0.,2.],
xlabel=r'$h^{\mathrm{in}}_R\ \mathrm{at}\ 8\,\mathrm{kpc}$',
ylabel=r'$h^{\mathrm{out}}_R / h^{\mathrm{in}}_R$'))
labels.append(r'$\sigma_R = %.0f \,\mathrm{km\,s}^{-1}$' % srs[0,indx])
for indx in range(1,options.nsr):
lines.append(bovy_plot.bovy_plot(hrs[:,indx],plotthis[:,indx],
color=colors[indx],ls=lss[indx],
overplot=True))
labels.append(r'$\sigma_R = %.0f \,\mathrm{km\,s}^{-1}$' % srs[0,indx])
"""
#Legend
pyplot.legend(lines,#(line1[0],line2[0],line3[0],line4[0]),
labels,#(r'$v_{bc} = 0$',
# r'$v_{bc} = 1\,\sigma_{bc}$',
# r'$v_{bc} = 2\,\sigma_{bc}$',
# r'$v_{bc} = 3\,\sigma_{bc}$'),
loc='lower right',#bbox_to_anchor=(.91,.375),
numpoints=2,
prop={'size':14},
frameon=False)
"""
#Add colorbar
map = cm.ScalarMappable(cmap=cm.jet)
map.set_array(srs[0,:])
map.set_clim(vmin=numpy.amin(srs[0,:]),vmax=numpy.amax(srs[0,:]))
cbar= pyplot.colorbar(map,fraction=0.15)
cbar.set_clim(numpy.amin(srs[0,:]),numpy.amax(srs[0,:]))
cbar.set_label(r'$\sigma_R \,(\mathrm{km\,s}^{-1})$')
bovy_plot.bovy_end_print(options.plotfilename)
def plot_szszvssz(options,args):
"""Plot sz^out/sz^in as a function of sz for various hr"""
if len(args) == 0.:
print "Must provide a savefilename ..."
print "Returning ..."
return None
if os.path.exists(args[0]):
#Load
savefile= open(args[0],'rb')
plotthis= pickle.load(savefile)
szs= pickle.load(savefile)
hrs= pickle.load(savefile)
savefile.close()
else:
#Grid of models to test
if options.subtype.lower() == 'sr':
szs= numpy.linspace(options.srmin,options.srmax,options.nsz)
else:
szs= numpy.linspace(options.szmin,options.szmax,options.nsz)
hrs= numpy.linspace(options.hrmin,options.hrmax,options.nhr)
#Tile
szs= numpy.tile(szs,(options.nhr,1)).T
hrs= numpy.tile(hrs,(options.nsz,1))
plotthis= numpy.zeros((options.nsz,options.nhr))
#Setup potential and aA
poptions= setup_options(None)
#poptions.potential= 'dpdiskplhalofixbulgeflatwgasalt'
#params= [0.,0.,0.,0.,0.,0.,-1.16315,1.,-3.,0.4,0.]
poptions.potential= 'btii'
params= None
#pot= MWPotential
pot= setup_potential(params,poptions,1)
if options.aAmethod.lower() == 'staeckel':
aA= actionAngleStaeckel(pot=pot,delta=0.45,c=True)
else:
aA=actionAngleAdiabaticGrid(pot=pot,nR=16,nEz=16,nEr=31,nLz=31,
zmax=1.,Rmax=5.)
for ii in range(options.nsz):
for jj in range(options.nhr):
if options.subtype.lower() == 'sr':
qdf= quasiisothermaldf(hrs[ii,jj]/8.,
szs[ii,jj]/220.,
szs[ii,jj]/220./numpy.sqrt(3.),
7./8.,1.,
pot=pot,aA=aA)
else:
qdf= quasiisothermaldf(hrs[ii,jj]/8.,
szs[ii,jj]/220.*numpy.sqrt(3.),
szs[ii,jj]/220.,
7./8.,1.,
pot=pot,aA=aA)
if options.subtype.lower() == 'sr':
plotthis[ii,jj]= numpy.sqrt(qdf.sigmaR2(1.,0.8/8.,
gl=True))/szs[ii,jj]*220.
else:
plotthis[ii,jj]= numpy.sqrt(qdf.sigmaz2(1.,0.8/8.,
gl=True))/szs[ii,jj]*220.
print ii*options.nhr+jj+1, options.nhr*options.nsz, \
szs[ii,jj], hrs[ii,jj], plotthis[ii,jj]
#Save
save_pickles(args[0],plotthis,szs,hrs)
#Now plot
bovy_plot.bovy_print(fig_width=6.,
text_fontsize=20.,
legend_fontsize=24.,
xtick_labelsize=18.,
ytick_labelsize=18.,
axes_labelsize=24.)
indx= 0
lines= []
colors= [cm.jet(ii/float(options.nhr-1.)*1.+0.) for ii in range(options.nhr)]
lss= ['-' for ii in range(options.nhr)]#,'--','-.','..']
labels= []
if options.subtype.lower() == 'sr':
lines.append(bovy_plot.bovy_plot(szs[:,indx],plotthis[:,indx],
color=colors[indx],ls=lss[indx],
xrange=[10.,85.],
yrange=[0.8,1.5],
xlabel=r'$\sigma^{\mathrm{in}}_R\ \mathrm{at}\ R = 8\,\mathrm{kpc}$',
ylabel=r'$\sigma^{\mathrm{out}}_R / \sigma^{\mathrm{in}}_R$'))
else:
lines.append(bovy_plot.bovy_plot(szs[:,indx],plotthis[:,indx],
color=colors[indx],ls=lss[indx],
xrange=[10.,115.],
yrange=[0.5,1.2],
xlabel=r'$\sigma^{\mathrm{in}}_Z\ \mathrm{at}\ R = 8\,\mathrm{kpc}$',
ylabel=r'$\sigma^{\mathrm{out}}_Z / \sigma^{\mathrm{in}}_Z$'))
for indx in range(1,options.nhr):
lines.append(bovy_plot.bovy_plot(szs[:,indx],plotthis[:,indx],
color=colors[indx],ls=lss[indx],
overplot=True))
#Add colorbar
map = cm.ScalarMappable(cmap=cm.jet)
map.set_array(hrs[0,:])
map.set_clim(vmin=numpy.amin(hrs[0,:]),vmax=numpy.amax(hrs[0,:]))
cbar= pyplot.colorbar(map,fraction=0.15)
cbar.set_clim(numpy.amin(hrs[0,:]),numpy.amax(hrs[0,:]))
cbar.set_label(r'$h_R\, (\mathrm{kpc})$')
bovy_plot.bovy_end_print(options.plotfilename)
def testDFNormvo(params,fehs,afes,binned,options,normintstuff):
"""Test how the normalization fares wrt hr changes"""
#setup potential
pot= setup_potential(params,options,len(fehs))
aA= setup_aA(pot,options)
ro= get_ro(params,options)
vo= get_vo(params,options,len(fehs))
#Assume there is only a single bin
indx= 0
dfparams= get_dfparams(params,indx,options,log=False)
nalt= 6
vos= numpy.linspace(0.5,1.5,nalt)
ns= [1000,10000,30000,60000,100000]#,1000000]
#First calculate default
if options.dfmodel.lower() == 'qdf':
#Normalize
hr= dfparams[0]/ro
sr= dfparams[1]/vo
sz= dfparams[2]/vo
hsr= dfparams[3]/ro
hsz= dfparams[4]/ro
#Setup
qdf= quasiisothermaldf(hr,sr,sz,hsr,hsz,pot=pot,aA=aA,cutcounter=True)
defNorm= numpy.zeros((nalt,len(ns)))
for ii, n in enumerate(ns):
print ii, n
options.nmc= n
thisnormintstuff= copy.deepcopy(normintstuff)
thisnormintstuff[indx].mock= normintstuff[indx].mock[0:n+1]
defNorm[:,ii]= numpy.log(calc_normint(qdf,indx,thisnormintstuff,params,len(fehs),options))
print defNorm[0,:]
#Then calculate alternative models
altNorm= numpy.zeros((nalt,len(ns)))
for ii in range(nalt):
potparams= list(get_potparams(params,options,len(fehs)))
potparams[0]= vo*vos[ii]
params= set_potparams(potparams,params,options,len(fehs))
if options.dfmodel.lower() == 'qdf':
#Normalize
sr= dfparams[1]/vo/vos[ii]
sz= dfparams[2]/vo/vos[ii]
#Setup
qdf= quasiisothermaldf(hr,sr,sz,hsr,hsz,pot=pot,aA=aA,
cutcounter=True)
for jj, n in enumerate(ns):
if n > len(normintstuff[indx].mock):
altNorm[ii,jj]= numpy.nan
continue
print ii, jj, n
options.nmc= n
thisnormintstuff= copy.deepcopy(normintstuff)
thisnormintstuff[indx].mock= normintstuff[indx].mock[0:n]
altNorm[ii,jj]= numpy.log(calc_normint(qdf,indx,thisnormintstuff,
params,len(fehs),options))
#Plot
left, bottom, width, height= 0.1, 0.3, 0.8, 0.6
axTop= pyplot.axes([left,bottom,width,height])
left, bottom, width, height= 0.1, 0.1, 0.8, 0.2
axSign= pyplot.axes([left,bottom,width,height])
fig= pyplot.gcf()
fig.sca(axTop)
pyplot.ylabel(r'$|\Delta \chi^2|$')
pyplot.xlim(ns[0]/5.,ns[-1]*5.)
nullfmt = NullFormatter() # no labels
axTop.xaxis.set_major_formatter(nullfmt)
pyplot.loglog(numpy.tile(numpy.array(ns),(nalt,1)).T,
numpy.fabs((defNorm-altNorm)*10000.).T,
marker='o',linestyle='none')
fig.sca(axSign)
pyplot.semilogx(numpy.tile(numpy.array(ns),(nalt,1)).T\
*(1.+0.4*(numpy.random.uniform(size=(len(ns),nalt))-0.5)),
numpy.fabs((defNorm-altNorm)).T/(defNorm-altNorm).T,
marker='o',linestyle='none')
pyplot.xlim(ns[0]/5.,ns[-1]*5.)
pyplot.ylim(-1.99,1.99)
pyplot.xlabel(r'$N$')
pyplot.ylabel(r'$\mathrm{sgn}(\Delta \chi^2)$')
bovy_plot.bovy_end_print(options.outfilename)
def generate_fakeDFData(options,args):
#Check whether the savefile already exists
if os.path.exists(args[0]):
savefile= open(args[0],'rb')
print "Savefile already exists, not re-sampling and overwriting ..."
return None
#Read the data
print "Reading the data ..."
if options.sample.lower() == 'g':
if options.select.lower() == 'program':
raw= read_gdwarfs(_GDWARFFILE,logg=True,ebv=True,sn=options.snmin,nosolar=True,nocoords=True)
else:
raw= read_gdwarfs(logg=True,ebv=True,sn=options.snmin,nosolar=True,nocoords=True)
elif options.sample.lower() == 'k':
if options.select.lower() == 'program':
raw= read_kdwarfs(_KDWARFFILE,logg=True,ebv=True,sn=options.snmin,nosolar=True,nocoords=True)
else:
raw= read_kdwarfs(logg=True,ebv=True,sn=options.snmin,nosolar=True,
nocoords=True)
if not options.bmin is None:
#Cut on |b|
raw= raw[(numpy.fabs(raw.b) > options.bmin)]
if not options.fehmin is None:
raw= raw[(raw.feh >= options.fehmin)]
if not options.fehmax is None:
raw= raw[(raw.feh < options.fehmax)]
if not options.afemin is None:
raw= raw[(raw.afe >= options.afemin)]
if not options.afemax is None:
raw= raw[(raw.afe < options.afemax)]
if not options.plate is None and not options.loo:
raw= raw[(raw.plate == options.plate)]
elif not options.plate is None:
raw= raw[(raw.plate != options.plate)]
#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
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 options.loo:
pass
else:
#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 ...")
data= copy.copy(binned.data[indx])
print "Using %i data points ..." % (len(data))
#Bin again
binned= pixelAfeFeh(data,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)
#Setup the selection function
#Load selection function
plates= numpy.array(list(set(list(raw.plate))),dtype='int') #Only load plates that we use
print "Using %i plates, %i stars ..." %(len(plates),len(raw))
sf= segueSelect(plates=plates,type_faint='tanhrcut',
sample=options.sample,type_bright='tanhrcut',
sn=options.snmin,select=options.select,
indiv_brightlims=options.indiv_brightlims)
platelb= bovy_coords.radec_to_lb(sf.platestr.ra,sf.platestr.dec,
degree=True)
if options.sample.lower() == 'g':
grmin, grmax= 0.48, 0.55
rmin,rmax= 14.50001, 20.199999 #so we don't go out of the range
if options.sample.lower() == 'k':
grmin, grmax= 0.55, 0.75
rmin,rmax= 14.50001, 18.999999
colorrange=[grmin,grmax]
mapfehs= monoAbundanceMW.fehs()
mapafes= monoAbundanceMW.afes()
#Setup params
if not options.init is None:
#Load initial parameters from file
savefile= open(options.init,'rb')
tparams= pickle.load(savefile)
savefile.close()
#Setup the correct form
params= initialize(options,fehs,afes)
params[0:6]= get_dfparams(tparams,options.index,options,log=True)
params[6:11]= tparams[-5:len(tparams)]
else:
params= initialize(options,fehs,afes)
#Setup potential
if (options.potential.lower() == 'flatlog' or options.potential.lower() == 'flatlogdisk') \
and not options.flatten is None:
#Set flattening
potparams= list(get_potparams(params,options,len(fehs)))
potparams[0]= options.flatten
params= set_potparams(potparams,params,options,len(fehs))
pot= setup_potential(params,options,len(fehs))
aA= setup_aA(pot,options)
if not options.multi is None:
binned= fakeDFData_abundance_singles(binned,options,args,fehs,afes)
else:
for ii in range(len(fehs)):
print "Working on population %i / %i ..." % (ii+1,len(fehs))
#Setup qdf
dfparams= get_dfparams(params,ii,options,log=False)
vo= get_vo(params,options,len(fehs))
ro= get_ro(params,options)
if options.dfmodel.lower() == 'qdf':
#Normalize
hr= dfparams[0]/ro
sr= dfparams[1]/vo
sz= dfparams[2]/vo
hsr= dfparams[3]/ro
hsz= dfparams[4]/ro
print hr, sr, sz, hsr, hsz
qdf= quasiisothermaldf(hr,sr,sz,hsr,hsz,pot=pot,aA=aA,cutcounter=True)
#Some more selection stuff
data= binned(fehs[ii],afes[ii])
#feh and color
feh= fehs[ii]
fehrange= [feh-options.dfeh/2.,feh+options.dfeh/2.]
#FeH
fehdist= DistSpline(*numpy.histogram(data.feh,bins=5,
range=fehrange),
xrange=fehrange,dontcuttorange=False)
#Color
colordist= DistSpline(*numpy.histogram(data.dered_g\
-data.dered_r,
bins=9,range=colorrange),
xrange=colorrange)
#Re-sample
binned= fakeDFData(binned,qdf,ii,params,fehs,afes,options,
rmin,rmax,
platelb,
grmin,grmax,
fehrange,
colordist,
fehdist,feh,sf,
mapfehs,mapafes,
ro=None,vo=None)
#Save to new file
fitsio.write(args[0],binned.data)
return None
