def calcAllSurfErrZ(ii,options,args):
#Go through all of the bins
if options.sample.lower() == 'g':
savefile= open('binmapping_g.sav','rb')
elif options.sample.lower() == 'k':
savefile= open('binmapping_k.sav','rb')
fehs= pickle.load(savefile)
afes= pickle.load(savefile)
savefile.close()
if numpy.log(monoAbundanceMW.hr(fehs[ii],afes[ii],
k=(options.sample.lower() == 'k')) /8.) > -0.5:
return numpy.zeros((101,4))
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]
#zs,mean_surfz, cov, std_surfz= calcSurfRdCorrZ(newname,vo=options.fixvc/_REFV0,
zs,mean_surfz, std_surfz= calcSurfErrZ(newname,vo=options.fixvc/_REFV0,
zh=options.fixzh,
dlnvcdlnr=options.dlnvcdlnr)
if zs is None: return numpy.zeros((101,4))
out= numpy.zeros((len(zs),4))
out[:,0]= zs
out[:,1]= mean_surfz
out[:,2]= std_surfz/mean_surfz
out[:,3]= std_surfz
return out
def plotprops(options,args):
#Go through all of the bins
if options.sample.lower() == 'g':
npops= 62
savefile= open('binmapping_g.sav','rb')
elif options.sample.lower() == 'k':
npops= 30
savefile= open('binmapping_k.sav','rb')
fehs= pickle.load(savefile)
afes= pickle.load(savefile)
ndatas= pickle.load(savefile)
savefile.close()
for ii in range(npops):
bovy_plot.bovy_print()
bovy_plot.bovy_text(r'$[\mathrm{Fe/H}] = %.2f$' % (fehs[ii])
+'\n'
r'$[\alpha/\mathrm{Fe}] = %.3f$' % (afes[ii])
+'\n'
r'$N_{\mathrm{data}} = %i$' % (ndatas[ii])
+'\n'
r'$\ln h_R / 8\,\mathrm{kpc} = %.1f$' % (numpy.log(monoAbundanceMW.hr(fehs[ii],afes[ii])/8.))
+'\n'
+r'$\ln \sigma_R / 220\,\mathrm{km\,s}^{-1} = %.1f$' % (numpy.log(monoAbundanceMW.sigmar(fehs[ii],afes[ii])/220.))
+'\n'
+r'$\ln \sigma_Z / 220\,\mathrm{km\,s}^{-1} = %.1f$' % (numpy.log(monoAbundanceMW.sigmaz(fehs[ii],afes[ii])/220.)),
top_left=True,size=16.)
#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)
def calcAllDerivProps(ii,options,args):
#Go through all of the bins
if options.sample.lower() == 'g':
savefile= open('binmapping_g.sav','rb')
elif options.sample.lower() == 'k':
savefile= open('binmapping_k.sav','rb')
fehs= pickle.load(savefile)
afes= pickle.load(savefile)
savefile.close()
if numpy.log(monoAbundanceMW.hr(fehs[ii],afes[ii],
k=(options.sample.lower() == 'k')) /8.) > -0.5:
return None
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]
derivProps= calcDerivProps(newname,vo=options.fixvc/_REFV0,zh=options.fixzh,
dlnvcdlnr=options.dlnvcdlnr)
return derivProps
def generateHTML(options,args):
outfile= open(options.outfilename,'w')
outfile.write('<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd">\n')
outfile.write('<html xmlns="http://www.w3.org/1999/xhtml">\n')
outfile.write('<head>\n')
outfile.write('<meta http-equiv="content-type" content="text/html; charset=utf-8" />\n')
outfile.write('<title>Fit results for %s dwarfs</title>\n' % (options.sample.upper()))
outfile.write('<meta name="description" content="Fit results for %s dwarfs" />\n' % (options.sample.upper()))
outfile.write('</head>')
outfile.write('<body>')
if options.sample.lower() == 'g':
npops= 62
elif options.sample.lower() == 'k':
npops= 54
if options.sample.lower() == 'g':
savefile= open('binmapping_g.sav','rb')
elif options.sample.lower() == 'k':
savefile= open('binmapping_k.sav','rb')
fehs= pickle.load(savefile)
afes= pickle.load(savefile)
npops= len(fehs)
savefile.close()
if False:
types= ['rdfh','rdfhrdvt','rdhr','rdsz',
'rdpout','rddvt','srsz','pout','dvt',
'loglhist','props','rdfhrdf','rdfhrdvtrdf']
if False:
types= ['rdfh','rdhr','rdhrc','rdsz',
'rdpout','rdpoutc','fhpoutc','rddvt','srsz','srszc','pout',
'loglhist','props']
types= ['rdfh','rdhr','rdhrc','rdsz',
'hszsz','hszszc','pout',#'hrreal',
'loglhist','derived','props']
ntypes= len(types)
for ii in range(npops):
if numpy.log(monoAbundanceMW.hr(fehs[ii],afes[ii],
k=(options.sample.lower() == 'k'))/8.) > -0.5:
continue
outfile.write('<p style="font-size:xx-large;">%i</p>\n' % ii)
line= ''
for jj in range(ntypes):
line+= '<img src="%s" alt="" width="300" /> ' % (os.path.join(options.figdir,options.basename+'_%s_%i.png' % (types[jj],ii)))
outfile.write(line+'<br>\n')
outfile.write('<hr size="3" color="black">\n')
outfile.write('</body>\n')
outfile.write('</html>\n')
outfile.close()
def calcAllPDFs(ii,options,args):
#Go through all of the bins
if options.sample.lower() == 'g':
savefile= open('binmapping_g.sav','rb')
elif options.sample.lower() == 'k':
savefile= open('binmapping_k.sav','rb')
fehs= pickle.load(savefile)
afes= pickle.load(savefile)
savefile.close()
if numpy.log(monoAbundanceMW.hr(fehs[ii],afes[ii],
k=(options.sample.lower() == 'k')) /8.) > -0.5 \
or (options.sample.lower() == 'g' and ii < 6) \
or (options.sample.lower() == 'k' and ii < 7):
return numpy.zeros((options.nrds,options.nfhs))
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:
return None
finally:
savefile.close()
if _NOTDONEYET:
logl[(logl == 0.)]= -numpy.finfo(numpy.dtype(numpy.float64)).max
logl[numpy.isnan(logl)]= -numpy.finfo(numpy.dtype(numpy.float64)).max
marglogl= numpy.zeros((logl.shape[0],logl.shape[3]))
for jj in range(marglogl.shape[0]):
for kk in range(marglogl.shape[1]):
marglogl[jj,kk]= misc.logsumexp(logl[jj,0,0,kk,:,:,:,0].flatten())
if marglogl[-1,-1] < -10000000000000.:
return numpy.zeros((options.nrds,options.nfhs))
else:
return marglogl
def plot1d(options,args):
"""Make a plot of a quantity's best-fit vs. FeH and aFe"""
if options.sample.lower() == 'g':
npops= 62
elif options.sample.lower() == 'k':
npops= 54
if options.sample.lower() == 'g':
savefile= open('binmapping_g.sav','rb')
elif options.sample.lower() == 'k':
savefile= open('binmapping_k.sav','rb')
fehs= pickle.load(savefile)
afes= pickle.load(savefile)
savefile.close()
#First calculate the derivative properties
if not options.multi is None:
derivProps= multi.parallel_map((lambda x: calcAllDerivProps(x,options,args)),
range(npops),
numcores=numpy.amin([options.multi,
npops,
multiprocessing.cpu_count()]))
else:
derivProps= []
for ii in range(npops):
derivProps.append(calcAllDerivProps(ii,options,args))
#Load into plotthis
plotthis= numpy.zeros(npops)+numpy.nan
for ii in range(npops):
if numpy.log(monoAbundanceMW.hr(fehs[ii],afes[ii],
k=(options.sample.lower() == 'k')) /8.) > -0.5 \
or (options.sample.lower() == 'g' and ii < 6) \
or (options.sample.lower() == 'k' and ii < 7):
continue
plotthis[ii]= derivProps[ii][options.subtype.lower()]
#Now plot
bovy_plot.bovy_print()
monoAbundanceMW.plotPixelFunc(fehs,afes,plotthis,
zlabel=labels[options.subtype.lower()])
bovy_plot.bovy_end_print(options.outfilename)
return None
colormap = matplotlib.cm.jet
def _squeeze(o,omin,omax):
return (o-omin)/(omax-omin)
d = pickle.load(open('01024/g1536.01024.z0agedecomp.dat'))
hz = d['hz']*1000
hzerr = d['hzerr']*1000
rexp = d['rexp'][(d['hzerr'] < 99)]
rexperr = d['rexperr'][(d['hzerr'] < 99)]
mass = d['mass'][(d['hzerr'] < 99)]
#Bovy stuff
bovyhz,bovyhzerr=zip(*[mam.hz(u[0],u[1],err=True) for u in zip(mam.fehs(),mam.afes())])
bovyrexp,bovyrexperr=zip(*[mam.hr(u[0],u[1],err=True) for u in zip(mam.fehs(),mam.afes())])
bovymass=np.array([mam.abundanceDist(u[0],u[1]) for u in zip(mam.fehs(),mam.afes())])
maxhr = 5
maxhz = 2000
bovyplotrexp = np.copy(bovyrexp)
bighr = bovyrexp > maxhr
bovyplotrexp[bighr] = maxhr-0.3
ax = plt.subplot(1,1,1)
ages = [i['mean'] for i in d['age']]
sizes = 10*(d['mass']/4e8)
print sizes
scat = plt.scatter(d['rexp'],hz,c=ages,s=sizes,edgecolors='none')
plt.xlim(1,6.5)
def plotRdfh(options,args):
#Go through all of the bins
if options.sample.lower() == 'g':
savefile= open('binmapping_g.sav','rb')
elif options.sample.lower() == 'k':
savefile= open('binmapping_k.sav','rb')
fehs= pickle.load(savefile)
afes= pickle.load(savefile)
npops= len(fehs)
savefile.close()
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
if options.restrictdvt:
logl= logl[:,:,:,:,:,:,:,1:4,:,:,:]
if options.restrictdf:
hrs= numpy.log(numpy.linspace(1.5,5.,logl.shape[4])/_REFR0)
srs= numpy.log(numpy.linspace(25.,70.,logl.shape[5])/_REFV0)
szs= numpy.log(numpy.linspace(15.,60.,logl.shape[6])/_REFV0)
lnhrin, lnsrin, lnszin= approxFitResult(fehs[ii],afes[ii])
hrindx= numpy.argmin((hrs-lnhrin)**2.)
srindx= numpy.argmin((srs-lnsrin)**2.)
szindx= numpy.argmin((szs-lnszin)**2.)
minhrindx= hrindx-1
maxhrindx= hrindx+1
if minhrindx < 0:
minhrindx+= 1
maxhrindx+= 1
elif maxhrindx >= logl.shape[4]:
minhrindx-= 1
maxhrindx-= 1
minsrindx= srindx-1
maxsrindx= srindx+1
if minsrindx < 0:
minsrindx+= 1
maxsrindx+= 1
elif maxsrindx >= logl.shape[5]:
minsrindx-= 1
maxsrindx-= 1
minszindx= szindx-1
maxszindx= szindx+1
if minszindx < 0:
minszindx+= 1
maxszindx+= 1
elif maxszindx >= logl.shape[6]:
minszindx-= 1
maxszindx-= 1
logl= logl[:,:,:,:,minhrindx:maxhrindx+1,minsrindx:maxsrindx+1,
minszindx:maxszindx+1,:,:,:,:]
marglogl= numpy.zeros((logl.shape[0],logl.shape[3]))
marglogldvt0= numpy.zeros((logl.shape[0],logl.shape[3]))
condfh= numpy.zeros((logl.shape[3]))
condlogp= numpy.zeros(logl.shape[0])
condfhdvt0= numpy.zeros((logl.shape[3]))
condlogpdvt0= numpy.zeros(logl.shape[0])
if ii == 0:
allmarglogl= numpy.zeros((logl.shape[0],logl.shape[3],npops))
for jj in range(marglogl.shape[0]):
for kk in range(marglogl.shape[1]):
indx= True-numpy.isnan(logl[jj,0,0,kk,:,:,:,:,:,:,:].flatten())
indxdvt0= True-numpy.isnan(logl[jj,0,0,kk,:,:,:,2,:,:,:].flatten())
if numpy.sum(indx) > 0:
marglogl[jj,kk]= misc.logsumexp(logl[jj,0,0,kk,:,:,:,:,:,:,:].flatten()[indx])
else:
marglogl[jj,kk]= -numpy.finfo(numpy.dtype(numpy.float64)).max
if numpy.sum(indxdvt0) > 0:
marglogldvt0[jj,kk]= misc.logsumexp(logl[jj,0,0,kk,:,:,:,2,:,:,:].flatten()[indxdvt0])
else:
marglogldvt0[jj,kk]= -numpy.finfo(numpy.dtype(numpy.float64)).max
condlogp[jj]= misc.logsumexp(marglogl[jj,:])
condlogl= marglogl[jj,:]-misc.logsumexp(marglogl[jj,:])
condfh[jj]= numpy.sum(numpy.exp(condlogl)*numpy.linspace(0.,1.,logl.shape[3]))/numpy.sum(numpy.exp(condlogl))
condlogpdvt0[jj]= misc.logsumexp(marglogldvt0[jj,:])
condlogldvt0= marglogldvt0[jj,:]-misc.logsumexp(marglogldvt0[jj,:])
condfhdvt0[jj]= numpy.sum(numpy.exp(condlogldvt0)*numpy.linspace(0.,1.,logl.shape[3]))/numpy.sum(numpy.exp(condlogldvt0))
if monoAbundanceMW.hr(fehs[ii],afes[ii]) < 3.5 \
and numpy.amax(logl) < 0.: #latter removes ridiculous bins
allmarglogl[:,:,ii]= marglogl
#Normalize
alogl= marglogl-numpy.amax(marglogl)
bovy_plot.bovy_print()
bovy_plot.bovy_dens2d(numpy.exp(alogl).T,
origin='lower',cmap='gist_yarg',
interpolation='nearest',
xrange=[1.5,4.5],yrange=[0.,1.],
xlabel=r'$R_d$',ylabel=r'$f_h$')
s= 2.*condlogp
s-= numpy.amax(s)
s+= 16.
s*= 3.
bovy_plot.bovy_plot(numpy.linspace(1.5,4.5,logl.shape[0]),
condfh,color='0.75',ls='-',
overplot=True,zorder=2)
bovy_plot.bovy_plot(numpy.linspace(1.5,4.5,logl.shape[0]),
condfh,color='0.75',marker='o',
s=s,scatter=True,overplot=True,zorder=10)
maxindx= numpy.argmax(s)
bovy_plot.bovy_plot(numpy.linspace(1.5,4.5,logl.shape[0])[maxindx],
condfh[maxindx],color='blue',marker='o',
ls='none',
ms=8.,mec='none',
overplot=True,zorder=13)
#dvt0
s= 2.*condlogpdvt0
s-= numpy.amax(s)
s+= 16.
s*= 3.
if not options.restrictdvt:
bovy_plot.bovy_plot(numpy.linspace(1.5,4.5,logl.shape[0]),
condfhdvt0,color='0.25',ls='-',
overplot=True,zorder=1)
bovy_plot.bovy_plot(numpy.linspace(1.5,4.5,logl.shape[0]),
condfhdvt0,color='0.25',marker='o',
s=s,scatter=True,overplot=True,zorder=11)
maxindx= numpy.argmax(s)
bovy_plot.bovy_plot(numpy.linspace(1.5,4.5,logl.shape[0])[maxindx],
condfhdvt0[maxindx],color='red',marker='o',
ls='none',
ms=8.,mec='none',
overplot=True,zorder=12)
#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]
if options.restrictdf and not options.restrictdvt:
bovy_plot.bovy_text(r'$\mathrm{Restricted\ DF\ parameters}$',
top_right=True,size=14.)
elif options.restrictdvt and not options.restrictdf:
bovy_plot.bovy_text(r'$\mathrm{Restricted}\ \Delta \bar{V}_T\ \mathrm{range}$',
top_right=True,size=14.)
elif options.restrictdvt and options.restrictdf:
bovy_plot.bovy_text(r'$\mathrm{Restricted\ DF\ parameters}$'
+'\n'
+r'$\mathrm{Restricted}\ \Delta \bar{V}_T\ \mathrm{range}$',
top_right=True,size=14.)
bovy_plot.bovy_end_print(newname)
#Now plot combined
alogl= numpy.sum(allmarglogl,axis=2)\
-numpy.amax(numpy.sum(allmarglogl,axis=2))
bovy_plot.bovy_print()
bovy_plot.bovy_dens2d(numpy.exp(alogl).T,
origin='lower',cmap='gist_yarg',
interpolation='nearest',
xrange=[1.5,4.5],yrange=[0.,1.],
xlabel=r'$R_d$',ylabel=r'$f_h$')
bovy_plot.bovy_end_print(options.outfilename)
def plot2d(options,args):
"""Make a plot of a quantity's best-fit vs. FeH and aFe"""
if options.sample.lower() == 'g':
npops= 62
elif options.sample.lower() == 'k':
npops= 54
if options.sample.lower() == 'g':
savefile= open('binmapping_g.sav','rb')
elif options.sample.lower() == 'k':
savefile= open('binmapping_k.sav','rb')
fehs= pickle.load(savefile)
afes= pickle.load(savefile)
savefile.close()
#First calculate the derivative properties
if not options.multi is None:
derivProps= multi.parallel_map((lambda x: calcAllDerivProps(x,options,args)),
range(npops),
numcores=numpy.amin([options.multi,
npops,
multiprocessing.cpu_count()]))
else:
derivProps= []
for ii in range(npops):
derivProps.append(calcAllDerivProps(ii,options,args))
xprop= options.subtype.split(',')[0]
yprop= options.subtype.split(',')[1]
if xprop == 'fracfaint' or yprop == 'fracfaint':
#Read the data
print "Reading the data ..."
raw= read_rawdata(options)
#Bin the data
binned= pixelAfeFeh(raw,dfeh=0.1,dafe=0.05)
for ii in range(npops):
if numpy.log(monoAbundanceMW.hr(fehs[ii],afes[ii],
k=(options.sample.lower() == 'k')) /8.) > -0.5 \
or (options.sample.lower() == 'g' and (ii == 50 or ii == 57)) \
or (options.sample.lower() == 'k' and ii < 7):
continue
data= binned(fehs[ii],afes[ii])
indx= (data.dered_r > 17.8)
derivProps[ii]['fracfaint']= numpy.sum(indx)/float(len(indx))
derivProps[ii]['fracfaint_err']= 0.
if xprop == 'nfaint' or yprop == 'nfaint':
#Read the data
print "Reading the data ..."
raw= read_rawdata(options)
#Bin the data
binned= pixelAfeFeh(raw,dfeh=0.1,dafe=0.05)
for ii in range(npops):
if numpy.log(monoAbundanceMW.hr(fehs[ii],afes[ii],
k=(options.sample.lower() == 'k')) /8.) > -0.5 \
or (options.sample.lower() == 'g' and ii < 6) \
or (options.sample.lower() == 'k' and ii < 7):
continue
data= binned(fehs[ii],afes[ii])
indx= (data.dered_r > 17.8)
derivProps[ii]['nfaint']= numpy.sum(indx)
derivProps[ii]['nfaint_err']= 0.
if xprop == 'hz' or yprop == 'hz':
for ii in range(npops):
if numpy.log(monoAbundanceMW.hr(fehs[ii],afes[ii],
k=(options.sample.lower() == 'k')) /8.) > -0.5 \
or (options.sample.lower() == 'g' and ii < 6) \
or (options.sample.lower() == 'k' and ii < 7):
continue
hz, hzerr= monoAbundanceMW.hz(fehs[ii],afes[ii],
k=(options.sample.lower() == 'k'),
err=True)
derivProps[ii]['hz']= hz
derivProps[ii]['hz_err']= hzerr
if xprop == 'hr' or yprop == 'hr':
for ii in range(npops):
if numpy.log(monoAbundanceMW.hr(fehs[ii],afes[ii],
k=(options.sample.lower() == 'k')) /8.) > -0.5 \
or (options.sample.lower() == 'g' and ii < 6) \
or (options.sample.lower() == 'k' and ii < 7):
continue
hr, hrerr= monoAbundanceMW.hr(fehs[ii],afes[ii],
k=(options.sample.lower() == 'k'),
err=True)
derivProps[ii]['hr']= hr
derivProps[ii]['hr_err']= hrerr
#Load into plotthis
plotthis_x= numpy.zeros(npops)+numpy.nan
plotthis_y= numpy.zeros(npops)+numpy.nan
plotthis_x_err= numpy.zeros(npops)+numpy.nan
plotthis_y_err= numpy.zeros(npops)+numpy.nan
for ii in range(npops):
if numpy.log(monoAbundanceMW.hr(fehs[ii],afes[ii],
k=(options.sample.lower() == 'k')) /8.) > -0.5 \
or (options.sample.lower() == 'g' and ii < 6) \
or (options.sample.lower() == 'k' and ii < 7):
continue
plotthis_x[ii]= derivProps[ii][xprop]
plotthis_y[ii]= derivProps[ii][yprop]
plotthis_x_err[ii]= derivProps[ii][xprop+'_err']
plotthis_y_err[ii]= derivProps[ii][yprop+'_err']
#Now plot
bovy_plot.bovy_print(fig_width=6.)
bovy_plot.bovy_plot(plotthis_x,plotthis_y,
s=25.,c=afes,
cmap='jet',
xlabel=labels[xprop],ylabel=labels[yprop],
clabel=r'$[\alpha/\mathrm{Fe}]$',
xrange=ranges[xprop],yrange=ranges[yprop],
vmin=0.,vmax=0.5,
scatter=True,edgecolors='none',
colorbar=True)
colormap = cm.jet
for ii in range(npops):
if numpy.isnan(plotthis_x[ii]): continue
pyplot.errorbar(plotthis_x[ii],
plotthis_y[ii],
xerr=plotthis_x_err[ii],
yerr=plotthis_y_err[ii],
color=colormap(_squeeze(afes[ii],
numpy.amax([numpy.amin(afes)]),
numpy.amin([numpy.amax(afes)]))),
elinewidth=1.,capsize=3,zorder=0)
bovy_plot.bovy_end_print(options.outfilename)
return None
def plotbestz(options,args):
"""Make a plot of a quantity's best-fit vs. FeH and aFe"""
if options.sample.lower() == 'g':
npops= 62
elif options.sample.lower() == 'k':
npops= 54
if options.sample.lower() == 'g':
savefile= open('binmapping_g.sav','rb')
elif options.sample.lower() == 'k':
savefile= open('binmapping_k.sav','rb')
fehs= pickle.load(savefile)
afes= pickle.load(savefile)
savefile.close()
#First calculate the derivative properties
if not options.multi is None:
derivProps= multi.parallel_map((lambda x: calcAllSurfErrZ(x,options,args)),
range(npops),
numcores=numpy.amin([options.multi,
npops,
multiprocessing.cpu_count()]))
else:
derivProps= []
for ii in range(npops):
derivProps.append(calcAllSurfErrZ(ii,options,args))
#Load into plotthis
plotthis= numpy.zeros(npops)+numpy.nan
plotthis_y= numpy.zeros(npops)+numpy.nan
plotthis_y_err= numpy.zeros(npops)+numpy.nan
for ii in range(npops):
if numpy.log(monoAbundanceMW.hr(fehs[ii],afes[ii],
k=(options.sample.lower() == 'k')) /8.) > -0.5 \
or (options.sample.lower() == 'g' and ii < 6) \
or (options.sample.lower() == 'k' and ii < 7):
continue
#Determine best-r
#indx= numpy.argmin(derivProps[ii][:,2]/numpy.fabs(derivProps[ii][:,1]))
indx= numpy.argmin(numpy.fabs(derivProps[ii][:,2]))
plotthis[ii]= derivProps[ii][indx,0]
plotthis_y[ii]= derivProps[ii][indx,1]
plotthis_y_err[ii]= derivProps[ii][indx,3]
#Now plot
bovy_plot.bovy_print()
print plotthis, plotthis_y
bovy_plot.bovy_plot(plotthis,plotthis_y,'ko',
xlabel=r'$Z\ (\mathrm{kpc})$',
ylabel=r'$\Sigma(R_0,|Z|)\ (M_\odot\,\mathrm{pc}^{-2})$',
xrange=[0.,5.],
yrange=[10.,1050.],#,numpy.nanmin(plotthis_y)-10.,
# numpy.nanmax(plotthis_y)+10.],
semilogy=True)
pyplot.errorbar(plotthis,
plotthis_y,
yerr=plotthis_y_err,
elinewidth=1.,capsize=3,zorder=0,
color='k',linestyle='none')
#trs= numpy.linspace(4.3,9.,1001)
#pyplot.plot(trs,72.*numpy.exp(-(trs-8.)/3.),'k--')
#pyplot.plot(trs,72.*numpy.exp(-(trs-8.)/2.),'k-.')
#pyplot.plot(trs,72.*numpy.exp(-(trs-8.)/4.),'k:')
bovy_plot.bovy_end_print(options.outfilename.replace('.png','_zvssurf.png'))
return None
def plotbestr(options,args):
"""Make a plot of a quantity's best-fit vs. FeH and aFe"""
if options.sample.lower() == 'g':
npops= 62
elif options.sample.lower() == 'k':
npops= 54
if options.sample.lower() == 'g':
savefile= open('binmapping_g.sav','rb')
elif options.sample.lower() == 'k':
savefile= open('binmapping_k.sav','rb')
fehs= pickle.load(savefile)
afes= pickle.load(savefile)
savefile.close()
#First calculate the derivative properties
if not options.multi is None:
derivProps= multi.parallel_map((lambda x: calcAllSurfErr(x,options,args)),
range(npops),
numcores=numpy.amin([options.multi,
npops,
multiprocessing.cpu_count()]))
else:
derivProps= []
for ii in range(npops):
derivProps.append(calcAllSurfErr(ii,options,args))
#If a second argument is given, this gives a set of rs at which also to calculate the surface density
if len(args) > 1:
if os.path.exists(args[1]):
surffile= open(args[1],'rb')
altsurfrs= pickle.load(surffile)
surffile.close()
calcExtra= True
else:
raise IOError("extra savefilename with surface-densities has to exist when it is specified")
else:
calcExtra= False
if not calcExtra: #Fiducial, for which we also calculate everything at the mean radius of each MAP
#Load g orbits
orbitsfile= 'gOrbitsNew.sav'
savefile= open(orbitsfile,'rb')
orbits= pickle.load(savefile)
savefile.close()
#Cut to S/N, logg, and EBV
indx= (orbits.sna > 15.)*(orbits.logga > 4.2)*(orbits.ebv < 0.3)
orbits= orbits[indx]
#Load the orbits into the pixel structure
pix= pixelAfeFeh(orbits,dfeh=0.1,dafe=0.05)
#Now calculate meanr
rmean= numpy.zeros(npops)
for ii in range(npops):
data= pix(fehs[ii],afes[ii])
vals= data.densrmean*8.
if False:#True:
rmean[ii]= numpy.mean(vals)
else:
rmean[ii]= numpy.median(vals)
#Load into plotthis
plotthis= numpy.zeros(npops)+numpy.nan
plotthis_y= numpy.zeros(npops)+numpy.nan
plotthis_y_err= numpy.zeros(npops)+numpy.nan
plotthiskz_y= numpy.zeros(npops)+numpy.nan
plotthiskz_y_err= numpy.zeros(npops)+numpy.nan
altplotthis= numpy.zeros(npops)+numpy.nan
altplotthis_y= numpy.zeros(npops)+numpy.nan
altplotthis_y_err= numpy.zeros(npops)+numpy.nan
altplotthiskz_y= numpy.zeros(npops)+numpy.nan
altplotthiskz_y_err= numpy.zeros(npops)+numpy.nan
for ii in range(npops):
if numpy.log(monoAbundanceMW.hr(fehs[ii],afes[ii],
k=(options.sample.lower() == 'k')) /8.) > -0.5 \
or (options.sample.lower() == 'g' and (ii < 0 or ii == 50)) \
or (options.sample.lower() == 'k' and ii < 7):
continue
#Determine best-r
#indx= numpy.argmin(derivProps[ii][:,2]/numpy.fabs(derivProps[ii][:,1]))
indx= numpy.argmin(numpy.fabs(derivProps[ii][:,2]))
if indx == 0: indx= int(numpy.floor(numpy.random.uniform()*10))
plotthis[ii]= derivProps[ii][indx,0]
plotthis_y[ii]= derivProps[ii][indx,1]
plotthis_y_err[ii]= derivProps[ii][indx,3]
plotthiskz_y[ii]= derivProps[ii][indx,4]
plotthiskz_y_err[ii]= derivProps[ii][indx,5]
if calcExtra:
indx= numpy.argmin(numpy.fabs(derivProps[ii][:,0]-altsurfrs[ii]))
altplotthis[ii]= derivProps[ii][indx,0]
altplotthis_y[ii]= derivProps[ii][indx,1]
altplotthis_y_err[ii]= derivProps[ii][indx,3]
altplotthiskz_y[ii]= derivProps[ii][indx,4]
altplotthiskz_y_err[ii]= derivProps[ii][indx,5]
else:
indx= numpy.argmin(numpy.fabs(derivProps[ii][:,0]-rmean[ii]))
altplotthis[ii]= derivProps[ii][indx,0]
altplotthis_y[ii]= derivProps[ii][indx,1]
altplotthis_y_err[ii]= derivProps[ii][indx,3]
altplotthiskz_y[ii]= derivProps[ii][indx,4]
altplotthiskz_y_err[ii]= derivProps[ii][indx,5]
#Now plot
bovy_plot.bovy_print()
monoAbundanceMW.plotPixelFunc(fehs,afes,plotthis,
zlabel=r'$R_\Sigma\ (\mathrm{kpc})$')
bovy_plot.bovy_end_print(options.outfilename)
bovy_plot.bovy_print()
print plotthis, plotthis_y
bovy_plot.bovy_plot(plotthis,plotthis_y,'ko',
xlabel=r'$R\ (\mathrm{kpc})$',
ylabel=r'$\Sigma(R,|Z| \leq 1.1\,\mathrm{kpc})\ (M_\odot\,\mathrm{pc}^{-2})$',
xrange=[4.,10.],
yrange=[10,1050.],#,numpy.nanmin(plotthis_y)-10.,
# numpy.nanmax(plotthis_y)+10.],
semilogy=True)
pyplot.errorbar(plotthis,
plotthis_y,
yerr=plotthis_y_err,
elinewidth=1.,capsize=3,zorder=0,
color='k',linestyle='none')
trs= numpy.linspace(4.3,9.,1001)
pyplot.plot(trs,72.*numpy.exp(-(trs-8.)/3.),'k--')
pyplot.plot(trs,72.*numpy.exp(-(trs-8.)/2.),'k-.')
pyplot.plot(trs,72.*numpy.exp(-(trs-8.)/4.),'k:')
#Fit exponential
#indx= (plotthis < 8.)
#plotthis= plotthis[indx]
#plotthis_y= plotthis_y[indx]
#plotthis_y_err= plotthis_y_err[indx]
exp_params= optimize.fmin_powell(expcurve,
numpy.log(numpy.array([72.,2.5])),
args=(plotthis,plotthis_y,plotthis_y_err))
pyplot.plot(trs,numpy.exp(exp_params[0]-(trs-8.)/numpy.exp(exp_params[1])),
'k-',lw=2.)
print numpy.exp(exp_params)
bovy_plot.bovy_end_print(options.outfilename.replace('.png','_rvssurf.png'))
#Save
if calcExtra:
save_pickles(options.outfilename.replace('.png','_rvssurf.sav'),
plotthis,plotthis_y,plotthis_y_err,
plotthiskz_y,plotthiskz_y_err,
altplotthis,altplotthis_y,altplotthis_y_err,
altplotthiskz_y,altplotthiskz_y_err)
else:
save_pickles(options.outfilename.replace('.png','_rvssurf.sav'),
plotthis,plotthis_y,plotthis_y_err,
plotthiskz_y,plotthiskz_y_err,
altplotthis,altplotthis_y,altplotthis_y_err,
altplotthiskz_y,altplotthiskz_y_err)
return None
def plot_DFsingles(options,args):
raw= read_rawdata(options)
#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
#Map the bins with ndata > minndata in 1D
fehs, afes= [], []
counter= 0
abindx= numpy.zeros((len(binned.fehedges)-1,len(binned.afeedges)-1),
dtype='int')
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))
abindx[ii,jj]= counter
counter+= 1
nabundancebins= len(fehs)
fehs= numpy.array(fehs)
afes= numpy.array(afes)
#Load each solutions
sols= []
savename= args[0]
initname= options.init
for ii in range(nabundancebins):
spl= savename.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]
savefilename= newname
#Read savefile
try:
savefile= open(savefilename,'rb')
except IOError:
print "WARNING: MISSING ABUNDANCE BIN"
sols.append(None)
else:
sols.append(pickle.load(savefile))
savefile.close()
#Load samples as well
if options.mcsample:
#Do the same for init
spl= initname.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]
options.init= newname
mapfehs= monoAbundanceMW.fehs()
mapafes= monoAbundanceMW.afes()
#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= []
errors= []
else:
plotthis= numpy.zeros((tightbinned.npixfeh(),tightbinned.npixafe()))
for ii in range(tightbinned.npixfeh()):
for jj in range(tightbinned.npixafe()):
data= binned(tightbinned.feh(ii),tightbinned.afe(jj))
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
#Find abundance indx
fehindx= binned.fehindx(tightbinned.feh(ii))#Map onto regular binning
afeindx= binned.afeindx(tightbinned.afe(jj))
solindx= abindx[fehindx,afeindx]
monoabindx= numpy.argmin((tightbinned.feh(ii)-mapfehs)**2./0.01 \
+(tightbinned.afe(jj)-mapafes)**2./0.0025)
if sols[solindx] 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 options.type.lower() == 'q':
s= get_potparams(sols[solindx],options,1)
plotthis[ii,jj]= s[0]
if not options.flatten is None:
plotthis[ii,jj]/= options.flatten
elif options.type.lower() == 'vc':
if options.fixvo:
plotthis[ii,jj]= 1.
else:
s= get_potparams(sols[solindx],options,1)
plotthis[ii,jj]= s[1]
elif options.type.lower() == 'rd':
s= get_potparams(sols[solindx],options,1)
plotthis[ii,jj]= numpy.exp(s[0])
elif options.type.lower() == 'zh':
s= get_potparams(sols[solindx],options,1)
plotthis[ii,jj]= numpy.exp(s[2-(1-(options.fixvo is None))])
elif options.type.lower() == 'ndata':
plotthis[ii,jj]= len(data)
elif options.type.lower() == 'hr':
s= get_dfparams(sols[solindx],0,options)
plotthis[ii,jj]= s[0]*_REFR0
if options.relative:
thishr= monoAbundanceMW.hr(mapfehs[monoabindx],mapafes[monoabindx])
plotthis[ii,jj]/= thishr
elif options.type.lower() == 'sz':
s= get_dfparams(sols[solindx],0,options)
plotthis[ii,jj]= s[2]*_REFV0
if options.relative:
thissz= monoAbundanceMW.sigmaz(mapfehs[monoabindx],mapafes[monoabindx])
plotthis[ii,jj]/= thissz
elif options.type.lower() == 'sr':
s= get_dfparams(sols[solindx],0,options)
plotthis[ii,jj]= s[1]*_REFV0
if options.relative:
thissr= monoAbundanceMW.sigmaz(mapfehs[monoabindx],mapafes[monoabindx])*2.
plotthis[ii,jj]/= thissr
elif options.type.lower() == 'srsz':
#Setup everything
pot= setup_potential(sols[solindx],options,1)
vo= get_vo(sols[solindx],options,1)
ro= get_ro(sols[solindx],options)
aA= setup_aA(pot,options)
dfparams= get_dfparams(sols[solindx],0,options,log=False)
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)
plotthis[ii,jj]= numpy.sqrt(qdf.sigmaR2(1.,1./_REFR0/ro,
ngl=options.ngl,gl=True)\
/qdf.sigmaz2(1.,1./_REFR0/ro,
ngl=options.ngl,gl=True))
elif options.type.lower() == 'outfrac':
s= get_dfparams(sols[solindx],0,options)
plotthis[ii,jj]= s[5]
elif options.type.lower() == 'rhodm':
#Setup potential
pot= setup_potential(sols[solindx],options,1)
vo= get_vo(sols[solindx],options,1)
ro= get_ro(sols[solindx],options)
if 'mwpotential' in options.potential.lower():
plotthis[ii,jj]= pot[1].dens(1.,0.)*_REFV0**2.*vo**2./_REFR0**2./ro**2./4.302*10.**-3.
elif options.potential.lower() == 'mpdiskplhalofixbulgeflat':
plotthis[ii,jj]= pot[1].dens(1.,0.)*_REFV0**2.*vo**2./_REFR0**2./ro**2./4.302*10.**-3.
elif options.potential.lower() == 'mpdiskflplhalofixplfixbulgeflat':
plotthis[ii,jj]= pot[1].dens(1.,0.)*_REFV0**2.*vo**2./_REFR0**2./ro**2./4.302*10.**-3.
elif options.type.lower() == 'rhoo':
#Setup potential
pot= setup_potential(sols[solindx],options,1)
vo= get_vo(sols[solindx],options,1)
ro= get_ro(sols[solindx],options)
plotthis[ii,jj]= potential.evaluateDensities(1.,0.,pot)*_REFV0**2.*vo**2./_REFR0**2./ro**2./4.302*10.**-3.
elif options.type.lower() == 'surfz':
#Setup potential
pot= setup_potential(sols[solindx],options,1)
vo= get_vo(sols[solindx],options,1)
ro= get_ro(sols[solindx],options)
plotthis[ii,jj]= 2.*integrate.quad((lambda zz: potential.evaluateDensities(1.,zz,pot)),0.,options.height/_REFR0/ro)[0]*_REFV0**2.*vo**2./_REFR0**2./ro**2./4.302*_REFR0*ro
elif options.type.lower() == 'surfzdisk':
#Setup potential
pot= setup_potential(sols[solindx],options,1)
vo= get_vo(sols[solindx],options,1)
ro= get_ro(sols[solindx],options)
if 'mpdisk' in options.potential.lower() or 'mwpotential' in options.potential.lower():
plotthis[ii,jj]= 2.*integrate.quad((lambda zz: potential.evaluateDensities(1.,zz,pot[0])),0.,options.height/_REFR0/ro)[0]*_REFV0**2.*vo**2./_REFR0**2./ro**2./4.302*_REFR0*ro
elif options.type.lower() == 'kz':
#Setup potential
pot= setup_potential(sols[solindx],options,1)
vo= get_vo(sols[solindx],options,1)
ro= get_ro(sols[solindx],options)
plotthis[ii,jj]= numpy.fabs(potential.evaluatezforces(1.,options.height/ro/_REFR0,pot)/2./numpy.pi/4.302*_REFV0**2.*vo**2./_REFR0/ro)
elif options.type.lower() == 'plhalo':
#Setup potential
pot= setup_potential(sols[solindx],options,1)
vo= get_vo(sols[solindx],options,1)
ro= get_ro(sols[solindx],options)
if options.potential.lower() == 'mpdiskplhalofixbulgeflat':
plotthis[ii,jj]= pot[1].alpha
elif options.type.lower() == 'qhalo':
#Setup potential
s= get_potparams(sols[solindx],options,1)
if options.potential.lower() == 'mpdiskflplhalofixplfixbulgeflat':
plotthis[ii,jj]= s[4]
elif options.type.lower() == 'dlnvcdlnr':
#Setup potential
pot= setup_potential(sols[solindx],options,1)
vo= get_vo(sols[solindx],options,1)
ro= get_ro(sols[solindx],options)
plotthis[ii,jj]= potential.dvcircdR(pot,1.)
elif options.type.lower() == 'fd':
#Setup potential
pot= setup_potential(sols[solindx],options,1)
vo= get_vo(sols[solindx],options,1)
ro= get_ro(sols[solindx],options)
if 'mwpotential' in options.potential.lower():
plotthis[ii,jj]= (pot[0].vcirc(1.))**2.
elif options.type.lower() == 'fh':
#Setup potential
pot= setup_potential(sols[solindx],options,1)
vo= get_vo(sols[solindx],options,1)
ro= get_ro(sols[solindx],options)
if 'mwpotential' in options.potential.lower():
plotthis[ii,jj]= (pot[1].vcirc(1.))**2.
elif options.type.lower() == 'fb':
#Setup potential
pot= setup_potential(sols[solindx],options,1)
vo= get_vo(sols[solindx],options,1)
ro= get_ro(sols[solindx],options)
if 'mwpotential' in options.potential.lower():
plotthis[ii,jj]= (pot[2].vcirc(1.))**2.
elif options.type.lower() == 'afe' or options.type.lower() == 'feh' or options.type.lower() == 'fehafe' \
or options.type.lower() == 'afefeh':
thisplot=[tightbinned.feh(ii),
tightbinned.afe(jj),
len(data)]
if options.subtype.lower() == 'qvc':
s= get_potparams(sols[solindx],options,1)
thisq= s[0]
if not options.flatten is None:
thisq/= options.flatten
thisvc= s[1]
thisplot.extend([thisq,thisvc])
elif options.subtype.lower() == 'rdzh':
s= get_potparams(sols[solindx],options,1)
thisrd= numpy.exp(s[0])
thiszh= numpy.exp(s[2-(1-(options.fixvo is None))])
thisplot.extend([thisrd,thiszh])
elif options.subtype.lower() == 'zhvc':
s= get_potparams(sols[solindx],options,1)
thiszh= numpy.exp(s[2-(1-(options.fixvo is None))])
thisvc= s[1]*_REFV0
thisplot.extend([thiszh,thisvc])
elif options.subtype.lower() == 'dlnvcdlnrvc':
s= get_potparams(sols[solindx],options,1)
thisslope= s[3-(1-(options.fixvo is None))]/30.
thisvc= s[1]*_REFV0
thisplot.extend([thisslope,thisvc])
elif options.subtype.lower() == 'rdvc':
s= get_potparams(sols[solindx],options,1)
thisrd= numpy.exp(s[0])
thisvc= s[1]*_REFV0
thisplot.extend([thisrd,thisvc])
elif options.subtype.lower() == 'rdplhalo':
s= get_potparams(sols[solindx],options,1)
thisrd= numpy.exp(s[0])
#Setup potential
pot= setup_potential(sols[solindx],options,1)
vo= get_vo(sols[solindx],options,1)
ro= get_ro(sols[solindx],options)
if options.potential.lower() == 'mpdiskplhalofixbulgeflat':
thisplhalo= pot[1].alpha
thisplot.extend([thisrd,thisplhalo])
elif options.subtype.lower() == 'dlnvcdlnrplhalo':
s= get_potparams(sols[solindx],options,1)
thisslope= s[3-(1-(options.fixvo is None))]/30.
#Setup potential
pot= setup_potential(sols[solindx],options,1)
vo= get_vo(sols[solindx],options,1)
ro= get_ro(sols[solindx],options)
if options.potential.lower() == 'mpdiskplhalofixbulgeflat':
thisplhalo= pot[1].alpha
thisplot.extend([thisslope,thisplhalo])
elif options.subtype.lower() == 'dlnvcdlnrzh':
s= get_potparams(sols[solindx],options,1)
thisslope= s[3-(1-(options.fixvo is None))]/30.
thiszh= numpy.exp(s[2-(1-(options.fixvo is None))])
thisplot.extend([thisslope,thiszh])
elif options.subtype.lower() == 'vc14plhalo':
s= get_potparams(sols[solindx],options,1)
#Setup potential
pot= setup_potential(sols[solindx],options,1)
vo= get_vo(sols[solindx],options,1)
ro= get_ro(sols[solindx],options)
if options.potential.lower() == 'mpdiskplhalofixbulgeflat':
thisplhalo= pot[1].alpha
thisvc14= potential.vcirc(pot,14./_REFR0/ro)*_REFV0*vo
thisplot.extend([thisplhalo,thisvc14])
elif options.subtype.lower() == 'plhalovc':
s= get_potparams(sols[solindx],options,1)
thisvc= s[1]*_REFV0
#Setup potential
pot= setup_potential(sols[solindx],options,1)
vo= get_vo(sols[solindx],options,1)
ro= get_ro(sols[solindx],options)
if options.potential.lower() == 'mpdiskplhalofixbulgeflat':
thisplhalo= pot[1].alpha
thisplot.extend([thisplhalo,thisvc])
elif options.subtype.lower() == 'zhplhalo':
s= get_potparams(sols[solindx],options,1)
thiszh= numpy.exp(s[2-(1-(options.fixvo is None))])
#Setup potential
pot= setup_potential(sols[solindx],options,1)
vo= get_vo(sols[solindx],options,1)
ro= get_ro(sols[solindx],options)
if options.potential.lower() == 'mpdiskplhalofixbulgeflat':
thisplhalo= pot[1].alpha
thisplot.extend([thiszh,thisplhalo])
elif options.subtype.lower() == 'rhodmzh':
s= get_potparams(sols[solindx],options,1)
thisrd= numpy.exp(s[0])
#Setup potential
pot= setup_potential(sols[solindx],options,1)
vo= get_vo(sols[solindx],options,1)
ro= get_ro(sols[solindx],options)
thiszh= numpy.exp(s[2-(1-(options.fixvo is None))])
if 'mwpotential' in options.potential.lower():
thisrhodm= pot[1].dens(1.,0.)*_REFV0**2.*vo**2./_REFR0**2./ro**2./4.302*10.**-3.
elif options.potential.lower() == 'mpdiskplhalofixbulgeflat':
thisrhodm= pot[1].dens(1.,0.)*_REFV0**2.*vo**2./_REFR0**2./ro**2./4.302*10.**-3.
thisplot.extend([thisrhodm,thiszh])
elif options.subtype.lower() == 'rhodmsurfz':
s= get_potparams(sols[solindx],options,1)
thisrd= numpy.exp(s[0])
#Setup potential
pot= setup_potential(sols[solindx],options,1)
vo= get_vo(sols[solindx],options,1)
ro= get_ro(sols[solindx],options)
thissurfz= 2.*integrate.quad((lambda zz: potential.evaluateDensities(1.,zz,pot)),0.,options.height/_REFR0/ro)[0]*_REFV0**2.*vo**2./_REFR0**2./ro**2./4.302*_REFR0*ro
if 'mwpotential' in options.potential.lower():
thisrhodm= pot[1].dens(1.,0.)*_REFV0**2.*vo**2./_REFR0**2./ro**2./4.302*10.**-3.
elif options.potential.lower() == 'mpdiskplhalofixbulgeflat':
thisrhodm= pot[1].dens(1.,0.)*_REFV0**2.*vo**2./_REFR0**2./ro**2./4.302*10.**-3.
thisplot.extend([thisrhodm,thissurfz])
elif options.subtype.lower() == 'surfzzh':
s= get_potparams(sols[solindx],options,1)
thisrd= numpy.exp(s[0])
#Setup potential
pot= setup_potential(sols[solindx],options,1)
vo= get_vo(sols[solindx],options,1)
ro= get_ro(sols[solindx],options)
thiszh= numpy.exp(s[2-(1-(options.fixvo is None))])
thissurfz= 2.*integrate.quad((lambda zz: potential.evaluateDensities(1.,zz,pot)),0.,options.height/_REFR0/ro)[0]*_REFV0**2.*vo**2./_REFR0**2./ro**2./4.302*_REFR0*ro
thisplot.extend([thissurfz,thiszh])
elif options.subtype.lower() == 'rhoozh':
s= get_potparams(sols[solindx],options,1)
thisrd= numpy.exp(s[0])
#Setup potential
pot= setup_potential(sols[solindx],options,1)
vo= get_vo(sols[solindx],options,1)
ro= get_ro(sols[solindx],options)
thiszh= numpy.exp(s[2-(1-(options.fixvo is None))])
thisrhoo= potential.evaluateDensities(1.,0.,pot)*_REFV0**2.*vo**2./_REFR0**2./ro**2./4.302*10.**-3.
thisplot.extend([thisrhoo,thiszh])
elif options.subtype.lower() == 'rhodmvc':
s= get_potparams(sols[solindx],options,1)
thisvc= s[1]*_REFV0
#Setup potential
pot= setup_potential(sols[solindx],options,1)
vo= get_vo(sols[solindx],options,1)
ro= get_ro(sols[solindx],options)
if 'mwpotential' in options.potential.lower():
thisrhodm= pot[1].dens(1.,0.)*_REFV0**2.*vo**2./_REFR0**2./ro**2./4.302*10.**-3.
elif options.potential.lower() == 'mpdiskplhalofixbulgeflat':
thisrhodm= pot[1].dens(1.,0.)*_REFV0**2.*vo**2./_REFR0**2./ro**2./4.302*10.**-3.
thisplot.extend([thisrhodm,thisvc])
elif options.subtype.lower() == 'rhodmrd':
s= get_potparams(sols[solindx],options,1)
thisrd= numpy.exp(s[0])
#Setup potential
pot= setup_potential(sols[solindx],options,1)
vo= get_vo(sols[solindx],options,1)
ro= get_ro(sols[solindx],options)
thisrdh= numpy.exp(s[0])
if 'mwpotential' in options.potential.lower():
thisrhodm= pot[1].dens(1.,0.)*_REFV0**2.*vo**2./_REFR0**2./ro**2./4.302*10.**-3.
elif options.potential.lower() == 'mpdiskplhalofixbulgeflat':
thisrhodm= pot[1].dens(1.,0.)*_REFV0**2.*vo**2./_REFR0**2./ro**2./4.302*10.**-3.
thisplot.extend([thisrhodm,thisrd])
elif options.subtype.lower() == 'rhodmplhalo':
s= get_potparams(sols[solindx],options,1)
thisrd= numpy.exp(s[0])
#Setup potential
pot= setup_potential(sols[solindx],options,1)
vo= get_vo(sols[solindx],options,1)
ro= get_ro(sols[solindx],options)
if options.potential.lower() == 'mpdiskplhalofixbulgeflat':
thisplhalo= pot[1].alpha
if 'mwpotential' in options.potential.lower():
thisrhodm= pot[1].dens(1.,0.)*_REFV0**2.*vo**2./_REFR0**2./ro**2./4.302*10.**-3.
elif options.potential.lower() == 'mpdiskplhalofixbulgeflat':
thisrhodm= pot[1].dens(1.,0.)*_REFV0**2.*vo**2./_REFR0**2./ro**2./4.302*10.**-3.
thisplot.extend([thisrhodm,thisplhalo])
plotthis.append(thisplot)
#Set up plot
if options.type.lower() == 'q':
if not options.flatten is None:
vmin, vmax= 0.9, 1.1
zlabel=r'$\mathrm{flattening}\ q / %.1f$' % options.flatten
elif 'real' in options.outfilename.lower():
vmin, vmax= 0.9, 1.1
medianq= numpy.median(plotthis[numpy.isfinite(plotthis)])
plotthis/= medianq
zlabel=r'$\mathrm{flattening}\ q / %.2f$' % medianq
else:
vmin, vmax= 0.5, 1.2
zlabel=r'$\mathrm{flattening}\ q$'
elif options.type.lower() == 'vc':
vmin, vmax= 0.95, 1.05
zlabel=r'$V_c / %i\ \mathrm{km\,s}^{-1}$' % int(_REFV0)
if 'real' in options.outfilename.lower():
medianvc= numpy.median(plotthis[numpy.isfinite(plotthis)])
plotthis/= medianvc
zlabel=r'$V_c / %i\ \mathrm{km\,s}^{-1}$' % int(_REFV0*medianvc)
elif options.type.lower() == 'rhodm':
vmin, vmax= 0.00, 0.02
zlabel=r'$\rho_{\mathrm{DM}}(R_0,0)\ [M_\odot\,\mathrm{pc}^{-3}]$'
elif options.type.lower() == 'rhoo':
vmin, vmax= 0.00, 0.2
zlabel=r'$\rho(R_0,0)\ [M_\odot\,\mathrm{pc}^{-3}]$'
elif options.type.lower() == 'surfz':
vmin, vmax= 50.,120.
zlabel=r'$\Sigma(%.1f\,\mathrm{kpc};R_0)\ [M_\odot\,\mathrm{pc}^{-2}]$' % options.height
elif options.type.lower() == 'surfzdisk':
vmin, vmax= 20.,90.
zlabel=r'$\Sigma_{\mathrm{disk}}(%.1f\,\mathrm{kpc};R_0)\ [M_\odot\,\mathrm{pc}^{-2}]$' % options.height
elif options.type.lower() == 'kz':
vmin, vmax= 50.,120.
zlabel=r'$K_Z(%.1f\,\mathrm{kpc};R_0)\ [M_\odot\,\mathrm{pc}^{-2}]$' % options.height
elif options.type.lower() == 'dlnvcdlnr':
vmin, vmax= -0.3,0.2
zlabel=r'$\frac{\mathrm{d} \ln V_c}{\mathrm{d} \ln R}$'
elif options.type.lower() == 'fd':
vmin, vmax= 0.00, 1.
zlabel=r'$V_{c,\mathrm{disk}} / V_c\,(R_0)$'
elif options.type.lower() == 'fh':
vmin, vmax= 0.00, 1.
zlabel=r'$V_{c,\mathrm{halo}} / V_c\,(R_0)$'
elif options.type.lower() == 'fb':
vmin, vmax= 0.00, .1
zlabel=r'$V_{c,\mathrm{halo}} / V_c\,(R_0)$'
elif options.type.lower() == 'rd':
vmin, vmax= 0.2, 0.6
zlabel=r'$R_d / R_0$'
elif options.type.lower() == 'zh':
vmin, vmax= 0.0125, 0.075
zlabel=r'$z_h / R_0$'
elif options.type.lower() == 'plhalo':
vmin, vmax= 0.0, 2.
zlabel=r'$\alpha\ \mathrm{in}\ \rho_{\mathrm{halo}} \propto 1/r^\alpha$'
elif options.type.lower() == 'qhalo':
vmin, vmax= 0.4, 1.15
zlabel=r'$q_\Phi^{\mathrm{halo}}$'
elif options.type.lower() == 'ndata':
vmin, vmax= numpy.nanmin(plotthis), numpy.nanmax(plotthis)
zlabel=r'$N_\mathrm{data}$'
elif options.type == 'hr':
if options.relative:
vmin, vmax= 0.8, 1.2
zlabel=r'$\mathrm{input / output\ radial\ scale\ length}$'
else:
vmin, vmax= 1.35,4.5
zlabel=r'$\mathrm{model\ radial\ scale\ length\ [kpc]}$'
elif options.type == 'sz':
if options.relative:
vmin, vmax= 0.8, 1.2
zlabel= r'$\mathrm{input / output\ model}\ \sigma_z$'
else:
vmin, vmax= 10.,60.
zlabel= r'$\mathrm{model}\ \sigma_z\ [\mathrm{km\ s}^{-1}]$'
elif options.type == 'sr':
if options.relative:
vmin, vmax= 0.8, 1.2
zlabel= r'$\mathrm{input/output\ model}\ \sigma_R$'
else:
vmin, vmax= 10.,60.
zlabel= r'$\mathrm{model}\ \sigma_R\ [\mathrm{km\ s}^{-1}]$'
elif options.type == 'srsz':
vmin, vmax= 0.5,2.
zlabel= r'$\sigma_R/\sigma_z\ (R_0,1\,\mathrm{kpc})$'
elif options.type == 'outfrac':
vmin, vmax= 0., 1.
zlabel= r'$\mathrm{relative\ number\ of\ outliers}$'
elif options.type == 'afe':
vmin, vmax= 0.0,.5
zlabel=r'$[\alpha/\mathrm{Fe}]$'
elif options.type == 'feh':
vmin, vmax= -1.6,0.4
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.,0.5]
yrange=[-0.2,0.6]
#Now plot
if options.type.lower() == 'afe' or options.type.lower() == 'feh' \
or options.type.lower() == 'fehafe':
#Gather everything
afe, feh, ndata, x, y= [], [], [], [], []
for ii in range(len(plotthis)):
afe.append(plotthis[ii][1])
feh.append(plotthis[ii][0])
ndata.append(plotthis[ii][2])
x.append(plotthis[ii][3])
y.append(plotthis[ii][4])
afe= numpy.array(afe)
feh= numpy.array(feh)
ndata= numpy.array(ndata)
x= numpy.array(x)
y= numpy.array(y)
#Process ndata
ndata= ndata**.5
ndata= ndata/numpy.median(ndata)*35.
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
onedhists=False
if options.subtype.lower() == 'qvc':
if not options.flatten is None:
xrange= [0.9,1.1]
xlabel=r'$\mathrm{flattening}\ q / %.1f$' % options.flatten
elif 'real' in options.outfilename.lower():
xrange= [0.9,1.1]
medianq= numpy.median(x[numpy.isfinite(x)])
x/= medianq
xlabel=r'$\mathrm{flattening}\ q / %.2f$' % medianq
else:
xrange= [0.5, 1.2]
xlabel=r'$\mathrm{flattening}\ q$'
yrange= [0.95, 1.05]
ylabel=r'$V_c / %i\ \mathrm{km\,s}^{-1}$' % int(_REFV0)
if 'real' in options.outfilename.lower():
medianvc= numpy.median(y[numpy.isfinite(y)])
y/= medianvc
ylabel=r'$V_c / %i\ \mathrm{km\,s}^{-1}$' % int(_REFV0*medianvc)
elif options.subtype.lower() == 'rdzh':
yrange= [0.0125,0.1]
xrange= [0.2,0.8]
xlabel=r'$R_d / R_0$'
ylabel=r'$z_h / R_0$'
elif options.subtype.lower() == 'rdplhalo':
yrange= [0.,2.]
xrange= [0.2,0.8]
xlabel=r'$R_d / R_0$'
ylabel=r'$\alpha\ \mathrm{in}\ \rho_{\mathrm{halo}} \propto 1/r^\alpha$'
elif options.subtype.lower() == 'vc14plhalo':
yrange= [210.,280.]
xrange= [0.,2.]
xlabel=r'$\alpha\ \mathrm{in}\ \rho_{\mathrm{halo}} \propto 1/r^\alpha$'
ylabel=r'$V_c (R=14\,\mathrm{kpc})\ [\mathrm{km\,s}^{-1}$]'
elif options.subtype.lower() == 'zhplhalo':
yrange= [0.,2.]
xrange= [0.0125,0.1]
ylabel=r'$\alpha\ \mathrm{in}\ \rho_{\mathrm{halo}} \propto 1/r^\alpha$'
xlabel=r'$z_h / R_0$'
elif options.subtype.lower() == 'rhodmplhalo':
xrange= [0.,0.02]
yrange= [0.,2.]
ylabel=r'$\alpha\ \mathrm{in}\ \rho_{\mathrm{halo}} \propto 1/r^\alpha$'
xlabel=r'$\rho_{\mathrm{DM}}(R_0,0)\ [M_\odot\,\mathrm{pc}^{-3}]$'
elif options.subtype.lower() == 'rhodmzh':
yrange= [0.0125,0.1]
xrange= [0.,0.02]
ylabel=r'$z_h / R_0$'
xlabel=r'$\rho_{\mathrm{DM}}(R_0,0)\ [M_\odot\,\mathrm{pc}^{-3}]$'
elif options.subtype.lower() == 'rhoozh':
yrange= [0.0125,0.1]
xrange= [0.,0.2]
ylabel=r'$z_h / R_0$'
xlabel=r'$\rho(R_0,0)\ [M_\odot\,\mathrm{pc}^{-3}]$'
elif options.subtype.lower() == 'surfzzh':
yrange= [0.0125,0.1]
xrange= [50.+20.*(options.height-1.1),120.+20.*(options.height-1.1)]
ylabel=r'$z_h / R_0$'
xlabel=r'$\Sigma(%.1f\,\mathrm{kpc};R_0)\ [M_\odot\,\mathrm{pc}^{-2}]$' % options.height
elif options.subtype.lower() == 'rhodmsurfz':
yrange= [50.+20.*(options.height-1.1),120.+20.*(options.height-1.1)]
xrange= [0.,0.02]
ylabel=r'$\Sigma(%.1f\,\mathrm{kpc};R_0)\ [M_\odot\,\mathrm{pc}^{-2}]$' % options.height
xlabel=r'$\rho_{\mathrm{DM}}(R_0,0)\ [M_\odot\,\mathrm{pc}^{-3}]$'
elif options.subtype.lower() == 'rhodmrd':
yrange= [0.2,0.8]
xrange= [0.,0.02]
ylabel=r'$R_d / R_0$'
xlabel=r'$\rho_{\mathrm{DM}}(R_0,0)\ [M_\odot\,\mathrm{pc}^{-3}]$'
elif options.subtype.lower() == 'rdvc':
yrange= [210.,250.]
xrange= [0.2,0.8]
xlabel=r'$R_d / R_0$'
ylabel=r'$V_c\ [\mathrm{km\,s}^{-1}]$'
elif options.subtype.lower() == 'zhvc':
yrange= [210.,250.]
xrange= [0.0125,0.1]
xlabel=r'$z_h / R_0$'
ylabel=r'$V_c\ [\mathrm{km\,s}^{-1}]$'
elif options.subtype.lower() == 'dlnvcdlnrvc':
yrange= [210.,250.]
xrange= [-0.2,0.07]
xlabel=r'$\mathrm{d}\ln V_c / \mathrm{d}\ln R\, (R_0)$'
ylabel=r'$V_c\ [\mathrm{km\,s}^{-1}]$'
onedhists=True
elif options.subtype.lower() == 'dlnvcdlnrplhalo':
yrange= [0.,2.]
ylabel=r'$\alpha\ \mathrm{in}\ \rho_{\mathrm{halo}} \propto 1/r^\alpha$'
xrange= [-0.2,0.07]
xlabel=r'$\mathrm{d}\ln V_c / \mathrm{d}\ln R\, (R_0)$'
elif options.subtype.lower() == 'dlnvcdlnrzh':
yrange= [0.0125,0.1]
ylabel=r'$z_h / R_0$'
xrange= [-0.2,0.07]
xlabel=r'$\mathrm{d}\ln V_c / \mathrm{d}\ln R\, (R_0)$'
elif options.subtype.lower() == 'rhodmvc':
yrange= [210.,250.]
xrange= [0.,0.02]
ylabel=r'$V_c\ [\mathrm{km\,s}^{-1}]$'
xlabel=r'$\rho_{\mathrm{DM}}(R_0,0)\ [M_\odot\,\mathrm{pc}^{-3}]$'
elif options.subtype.lower() == 'plhalovc':
yrange= [210.,250.]
xrange= [0.,2.]
xlabel=r'$\alpha\ \mathrm{in}\ \rho_{\mathrm{halo}} \propto 1/r^\alpha$'
ylabel=r'$V_c\ [\mathrm{km\,s}^{-1}$]'
bovy_plot.bovy_print(fig_height=3.87,fig_width=5.)
ax= bovy_plot.bovy_plot(x,y,
s=ndata,c=plotc,
cmap='jet',
xlabel=xlabel,ylabel=ylabel,
clabel=zlabel,
xrange=xrange,yrange=yrange,
vmin=vmin,vmax=vmax,
scatter=True,edgecolors='none',
colorbar=True-onedhists,
onedhists=onedhists,
onedhistxnormed=onedhists,
onedhistynormed=onedhists,
bins=15)
if onedhists:
axS, axx, axy= ax
if options.subtype.lower() == 'dlnvcdlnrvc':
#Plot prior on one-d axes
sb= numpy.linspace(-0.2,0.0399,1001)
fsb= numpy.exp(numpy.log((0.04-sb)/0.04)-(0.04-sb)/0.04)
fsb/= numpy.sum(fsb)*(sb[1]-sb[0])
axx.plot(sb,fsb,'-',color='0.65')
tvc= numpy.linspace(150.,350.,1001)
fvc= numpy.exp(-(tvc-225.)**2./2./15.**2.)
fvc/= numpy.sum(fvc)*(tvc[1]-tvc[0])
axy.plot(fvc,tvc,'-',color='0.65')
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.type.lower() == 'q' or options.type.lower() == 'vc' \
or options.relative or options.type.lower() == 'rd' \
or options.type.lower() == 'fd' \
or options.type.lower() == 'fh' \
or options.type.lower() == 'fb' \
or options.type.lower() == 'plhalo' \
or options.type.lower() == 'surfz' \
or options.type.lower() == 'surfzdisk' \
or options.type.lower() == 'rhoo' \
or options.type.lower() == 'qhalo' \
or options.type.lower() == 'kz':
bovy_plot.bovy_text(r'$\mathrm{median} = %.2f \pm %.2f$' % (numpy.median(plotthis[numpy.isfinite(plotthis)]),
1.4826*numpy.median(numpy.fabs(plotthis[numpy.isfinite(plotthis)]-numpy.median(plotthis[numpy.isfinite(plotthis)])))),
bottom_left=True,size=14.)
if options.type.lower() == 'zh' or options.type.lower() == 'rhodm':
bovy_plot.bovy_text(r'$\mathrm{median} = %.4f \pm %.4f$' % (numpy.median(plotthis[numpy.isfinite(plotthis)]),
1.4826*numpy.median(numpy.fabs(plotthis[numpy.isfinite(plotthis)]-numpy.median(plotthis[numpy.isfinite(plotthis)])))),
bottom_left=True,size=14.)
bovy_plot.bovy_end_print(options.outfilename)
return None
def plotActionData(options,args):
#Read the data
print "Reading the data ..."
raw= read_rawdata(options)
#Setup error mc integration
options.nmcerr= 1#in case this isn't set correctly
raw, errstuff= setup_err_mc(raw,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)
#print numpy.argmin((fehs+0.25)**2.+(afes-0.175)**2.)
#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]
aA= setup_aA(pot,options)
jr, lz, jz= [], [], []
#Get data ready
params= [None,None,None,None,None,None,params[0],params[1],params[2],params[3],params[4]]
for indx in range(len(fehs)):
if numpy.log(monoAbundanceMW.hr(fehs[indx],afes[indx],k=(options.sample.lower() == 'k'))/8.) > -0.5 :
continue
R,vR,vT,z,vz,e,ee,eee= prepare_coordinates(params,indx,fehs,afes,binned,
errstuff,
options,1)
tjr, tlz, tjz= aA(R.flatten(),vR.flatten(),vT.flatten(),z.flatten(),vz.flatten())
jr.extend(list(tjr))
lz.extend(list(tlz))
jz.extend(list(tjz))
#Now plot
jr= numpy.array(jr).flatten()*ro*vo*_REFR0*_REFV0
lz= numpy.array(lz).flatten()*ro*vo*_REFR0*_REFV0
jz= numpy.array(jz).flatten()*ro*vo*_REFR0*_REFV0
bovy_plot.bovy_print()
levels= special.erf(numpy.sqrt(0.5)*numpy.arange(1,3))
levels= list(levels)
levels.append(1.01)
print len(jr)
if options.type.lower() == 'lzjr':
axScatter, axHistx,axHisty= bovy_plot.scatterplot(lz/220.,jr/220.,',',
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,
bins=41,
levels=levels,retAxes=True)
axScatter.set_xlim(0.,3600./220.)
axScatter.set_ylim(0.,500./220.)
axScatter.set_xlabel(r'$L_z\ (220\,\mathrm{km\,s}^{-1}\,\mathrm{kpc})$')
axScatter.set_ylabel(r'$J_R\ (220\,\mathrm{km\,s}^{-1}\,\mathrm{kpc})$')
axHistx.set_xlim( axScatter.get_xlim() )
axHisty.set_ylim( axScatter.get_ylim() )
#Calculate locus of 6 kpc pericenter
nlzs= 1001
plzs= numpy.linspace(0.,6./8.,nlzs)
pjrs= numpy.zeros(nlzs)
for ii in range(nlzs):
pjrs[ii]= aA(6./8.,0.,plzs[ii]/6.*8.,0.,0.)[0]*ro*vo*_REFR0*_REFV0
bovy_plot.bovy_plot(plzs*ro*vo*_REFR0*_REFV0/220.,
pjrs/220.,'k--',overplot=True)
plzs= numpy.linspace(11./8.,2.,nlzs)
pjrs= numpy.zeros(nlzs)
for ii in range(nlzs):
pjrs[ii]= aA(11./8.,0.,plzs[ii]/11.*8.,0.,0.)[0]*ro*vo*_REFR0*_REFV0
bovy_plot.bovy_plot(plzs*ro*vo*_REFR0*_REFV0/220.,
pjrs/220.,'k--',overplot=True)
elif options.type.lower() == 'jrjz':
axScatter, axHistx, axHisty= bovy_plot.scatterplot(jr/220.,jz/220.,color='k',marker=',',ls='none',
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.],
bins=41,
onedhists=True,
levels=levels,
retAxes=True)
axScatter.set_xlim(0.,500./220.)
axScatter.set_ylim(0.,250./220.)
axScatter.set_ylabel(r'$J_Z\ (220\,\mathrm{km\,s}^{-1}\,\mathrm{kpc})$')
axScatter.set_xlabel(r'$J_R\ (220\,\mathrm{km\,s}^{-1}\,\mathrm{kpc})$')
axHistx.set_xlim( axScatter.get_xlim() )
axHisty.set_ylim( axScatter.get_ylim() )
#if options.sample == 'g':
# bovy_plot.bovy_text(r'$\mathrm{G\!-\!type\ dwarfs}$',top_right=True,size=16.)
#elif options.sample == 'k':
# bovy_plot.bovy_text(r'$\mathrm{K\!-\!type\ dwarfs}$',top_right=True,size=16.)
bovy_plot.bovy_end_print(options.outfilename)
return None
def calcDFResults(options,args,boot=True,nomedian=False):
if len(args) == 2 and options.sample == 'gk':
toptions= copy.copy(options)
toptions.sample= 'g'
toptions.select= 'all'
outg= calcDFResults(toptions,[args[0]],boot=boot,nomedian=True)
toptions.sample= 'k'
toptions.select= 'program'
outk= calcDFResults(toptions,[args[1]],boot=boot,nomedian=True)
#Combine
out= {}
for k in outg.keys():
valg= outg[k]
valk= outk[k]
val= numpy.zeros(len(valg)+len(valk))
val[0:len(valg)]= valg
val[len(valg):len(valg)+len(valk)]= valk
out[k]= val
if nomedian: return out
else: return add_median(out,boot=boot)
raw= read_rawdata(options)
#Bin the data
binned= pixelAfeFeh(raw,dfeh=options.dfeh,dafe=options.dafe)
tightbinned= binned
#Map the bins with ndata > minndata in 1D
fehs, afes= [], []
counter= 0
abindx= numpy.zeros((len(binned.fehedges)-1,len(binned.afeedges)-1),
dtype='int')
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))
abindx[ii,jj]= counter
counter+= 1
nabundancebins= len(fehs)
fehs= numpy.array(fehs)
afes= numpy.array(afes)
#Load each of the solutions
sols= []
chi2s= []
savename= args[0]
initname= options.init
for ii in range(nabundancebins):
spl= savename.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]
savefilename= newname
#Read savefile
try:
savefile= open(savefilename,'rb')
except IOError:
print "WARNING: MISSING ABUNDANCE BIN"
sols.append(None)
chi2s.append(None)
else:
sols.append(pickle.load(savefile))
chi2s.append(pickle.load(savefile))
savefile.close()
#Load samples as well
if options.mcsample:
#Do the same for init
spl= initname.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]
options.init= newname
mapfehs= monoAbundanceMW.fehs()
mapafes= monoAbundanceMW.afes()
#Now plot
#Run through the pixels and gather
fehs= []
afes= []
ndatas= []
zmedians= []
#Basic parameters
hrs= []
srs= []
szs= []
hsrs= []
hszs= []
outfracs= []
rds= []
rdexps= []
vcs= []
zhs= []
zhexps= []
dlnvcdlnrs= []
plhalos= []
rorss= []
dvts= []
#derived parameters
surfzs= []
surfz800s= []
surfzdisks= []
massdisks= []
rhoos= []
rhooalts= []
rhodms= []
vcdvcros= []
vcdvcs= []
vescs= []
mloglikemins= []
for ii in range(tightbinned.npixfeh()):
for jj in range(tightbinned.npixafe()):
data= binned(tightbinned.feh(ii),tightbinned.afe(jj))
if len(data) < options.minndata:
continue
#Find abundance indx
fehindx= binned.fehindx(tightbinned.feh(ii))#Map onto regular binning
afeindx= binned.afeindx(tightbinned.afe(jj))
solindx= abindx[fehindx,afeindx]
monoabindx= numpy.argmin((tightbinned.feh(ii)-mapfehs)**2./0.01 \
+(tightbinned.afe(jj)-mapafes)**2./0.0025)
if sols[solindx] is None:
continue
try:
pot= setup_potential(sols[solindx],options,1,interpDens=True,
interpdvcircdr=True,returnrawpot=True)
except RuntimeError:
print "A bin has an unphysical potential ..."
continue
# if 'dpdisk' in options.potential.lower():
# try:
# rawpot= setup_potential(sols[solindx],options,1,
# returnrawpot=True)
# except RuntimeError:
# print "A bin has an unphysical potential ..."
# continue
fehs.append(tightbinned.feh(ii))
afes.append(tightbinned.afe(jj))
zmedians.append(numpy.median(numpy.fabs(data.zc+_ZSUN)))
#vc
s= get_potparams(sols[solindx],options,1)
ro= get_ro(sols[solindx],options)
if options.fixvo:
vcs.append(options.fixvo*_REFV0)
else:
vcs.append(s[1]*_REFV0)
#rd
rds.append(numpy.exp(s[0]))
#zh
zhs.append(numpy.exp(s[2-(1-(options.fixvo is None))]))
#rdexp & zhexp
if options.sample == 'g': tz= 1.1/_REFR0/ro
elif options.sample == 'k': tz= 0.84/_REFR0/ro
if 'mpdisk' in options.potential.lower() or 'mwpotential' in options.potential.lower():
mp= potential.MiyamotoNagaiPotential(a=rds[-1],b=zhs[-1])
#rdexp
f= mp.dens(1.,0.125)
dr= 10.**-3.
df= (mp.dens(1.+dr/2.,0.125)-mp.dens(1.-dr/2.,0.125))/dr
rdexps.append(-f/df)
#zhexp
f= mp.dens(1.,tz)
dz= 10.**-3.
df= (mp.dens(1.,tz+dz/2.)-mp.dens(1.,tz-dz/2.))/dz
zhexps.append(-f/df)
elif 'dpdisk' in options.potential.lower():
rdexps.append(numpy.exp(s[0]))
zhexps.append(numpy.exp(s[2-(1-(options.fixvo is None))]))
#ndata
ndatas.append(len(data))
#hr
dfparams= get_dfparams(sols[solindx],0,options)
if options.relative:
thishr= monoAbundanceMW.hr(mapfehs[monoabindx],mapafes[monoabindx])
hrs.append(dfparams[0]*_REFR0/thishr)
else:
hrs.append(dfparams[0]*_REFR0)
#sz
if options.relative:
thissz= monoAbundanceMW.sigmaz(mapfehs[monoabindx],mapafes[monoabindx])
szs.append(dfparams[2]*_REFV0/thissz)
else:
szs.append(dfparams[2]*_REFV0)
#sr
if options.relative:
thissr= monoAbundanceMW.sigmaz(mapfehs[monoabindx],mapafes[monoabindx])*2.#BOVY: UPDATE
srs.append(dfparams[1]*_REFV0/thissr)
else:
srs.append(dfparams[1]*_REFV0)
#hsr
hsrs.append(dfparams[3]*_REFR0)
#hsz
hszs.append(dfparams[4]*_REFR0)
#outfrac
outfracs.append(dfparams[5])
#rhodm
#Setup potential
vo= get_vo(sols[solindx],options,1)
if 'mwpotential' in options.potential.lower():
rhodms.append(pot[1].dens(1.,0.)*_REFV0**2.*vo**2./_REFR0**2./ro**2./4.302*10.**-3.)
elif options.potential.lower() == 'mpdiskplhalofixbulgeflat':
rhodms.append(pot[1].dens(1.,0.)*_REFV0**2.*vo**2./_REFR0**2./ro**2./4.302*10.**-3.)
elif options.potential.lower() == 'mpdiskflplhalofixplfixbulgeflat':
rhodms.append(pot[1].dens(1.,0.)*_REFV0**2.*vo**2./_REFR0**2./ro**2./4.302*10.**-3.)
elif options.potential.lower() == 'dpdiskplhalofixbulgeflat' \
or options.potential.lower() == 'dpdiskplhalofixbulgeflatwgas' \
or options.potential.lower() == 'dpdiskplhalofixbulgeflatwgasalt' \
or options.potential.lower() == 'dpdiskflplhalofixbulgeflatwgas':
rhodms.append(pot[1].dens(1.,0.)*_REFV0**2.*vo**2./_REFR0**2./ro**2./4.302*10.**-3.)
#rhoo
rhoos.append(potential.evaluateDensities(1.,0.,pot)*_REFV0**2.*vo**2./_REFR0**2./ro**2./4.302*10.**-3.)
#surfz
surfzs.append(2.*integrate.quad((lambda zz: potential.evaluateDensities(1.,zz,pot)),0.,options.height/_REFR0/ro)[0]*_REFV0**2.*vo**2./_REFR0**2./ro**2./4.302*_REFR0*ro)
#surfz800
surfz800s.append(2.*integrate.quad((lambda zz: potential.evaluateDensities(1.,zz,pot)),0.,0.8/_REFR0/ro)[0]*_REFV0**2.*vo**2./_REFR0**2./ro**2./4.302*_REFR0*ro)
#surzdisk
if 'mpdisk' in options.potential.lower() or 'mwpotential' in options.potential.lower():
surfzdisks.append(2.*integrate.quad((lambda zz: potential.evaluateDensities(1.,zz,pot[0])),0.,options.height/_REFR0/ro)[0]*_REFV0**2.*vo**2./_REFR0**2./ro**2./4.302*_REFR0*ro)
surfzdiskzm= 2.*integrate.quad((lambda zz: potential.evaluateDensities(1.,zz,pot[0])),0.,tz)[0]*_REFV0**2.*vo**2./_REFR0**2./ro**2./4.302*_REFR0*ro
elif 'dpdisk' in options.potential.lower():
surfzdisks.append(2.*pot[0].dens(1.,0.)*_REFV0**2.*vo**2./_REFR0**2./ro**2./4.302*10.**-3.*zhexps[-1]*ro*_REFR0*1000.)
#rhooalt
if options.potential.lower() == 'mpdiskplhalofixbulgeflat':
rhooalts.append(rhoos[-1]-pot[0].dens(1.,0.)*_REFV0**2.*vo**2./_REFR0**2./ro**2./4.302*10.**-3.+surfzdiskzm/2./zhexps[-1]/ro/_REFR0/1000./(1.-numpy.exp(-tz/zhexps[-1])))
elif options.potential.lower() == 'dpdiskplhalofixbulgeflat' \
or options.potential.lower() == 'dpdiskplhalofixbulgeflatwgas' \
or options.potential.lower() == 'dpdiskplhalofixbulgeflatwgasalt' \
or options.potential.lower() == 'dpdiskflplhalofixbulgeflatwgas':
rhooalts.append(rhoos[-1])
#massdisk
if options.potential.lower() == 'dpdiskplhalofixbulgeflat' \
or options.potential.lower() == 'dpdiskplhalofixbulgeflatwgas' \
or options.potential.lower() == 'dpdiskplhalofixbulgeflatwgasalt' \
or options.potential.lower() == 'dpdiskflplhalofixbulgeflatwgas':
rhod= pot[0].dens(1.,0.)*_REFV0**2.*vo**2./_REFR0**2./ro**2./4.302*10.**-3.
else:
rhod= surfzdiskzm/2./zhexps[-1]/ro/_REFR0/1000./(1.-numpy.exp(-tz/zhexps[-1]))
massdisks.append(rhod*2.*zhexps[-1]*numpy.exp(1./rdexps[-1])*rdexps[-1]**2.*2.*numpy.pi*(ro*_REFR0)**3./10.)
#plhalo
if options.potential.lower() == 'mpdiskplhalofixbulgeflat':
plhalos.append(pot[1].alpha)
plhalos.append((1.-pot[1].alpha)/(pot[1].alpha-3.))
elif options.potential.lower() == 'dpdiskplhalofixbulgeflat' \
or options.potential.lower() == 'dpdiskplhalofixbulgeflatwgas' \
or options.potential.lower() == 'dpdiskplhalofixbulgeflatwgasalt' \
or options.potential.lower() == 'dpdiskflplhalofixbulgeflatwgas':
plhalos.append(pot[1].alpha)
rorss.append((1.-pot[1].alpha)/(pot[1].alpha-3.))
#dlnvcdlnr
if options.potential.lower() == 'dpdiskplhalofixbulgeflat' \
or options.potential.lower() == 'dpdiskplhalofixbulgeflatwgas' \
or options.potential.lower() == 'dpdiskplhalofixbulgeflatwgasalt' \
or options.potential.lower() == 'dpdiskflplhalofixbulgeflatwgas':
dlnvcdlnrs.append(potential.dvcircdR(pot,1.))
else:
dlnvcdlnrs.append(potential.dvcircdR(pot,1.))
#vcdvc
if options.potential.lower() == 'dpdiskplhalofixbulgeflat' \
or options.potential.lower() == 'dpdiskplhalofixbulgeflatwgas' \
or options.potential.lower() == 'dpdiskplhalofixbulgeflatwgasalt' \
or options.potential.lower() == 'dpdiskflplhalofixbulgeflatwgas':
vcdvcros.append(pot[0].vcirc(1.)/potential.vcirc(pot,1.))
vcdvcs.append(pot[0].vcirc(2.2*rdexps[-1])/potential.vcirc(pot,2.2*rdexps[-1]))
else:
vcdvcros.append(pot[0].vcirc(1.)/potential.vcirc(pot,1.))
vcdvcs.append(pot[0].vcirc(2.2*rdexps[-1])/potential.vcirc(pot,2.2*rdexps[-1]))
#mloglike
mloglikemins.append(chi2s[solindx])
#escape velocity
vescs.append(potential.vesc(pot,1.)*_REFV0)
if options.fitdvt:
dvts.append(sols[solindx][0])
#Gather
fehs= numpy.array(fehs)
afes= numpy.array(afes)
zmedians= numpy.array(zmedians)
ndatas= numpy.array(ndatas)
#Basic parameters
hrs= numpy.array(hrs)
srs= numpy.array(srs)
szs= numpy.array(szs)
hsrs= numpy.array(hsrs)
hszs= numpy.array(hszs)
outfracs= numpy.array(outfracs)
vcs= numpy.array(vcs)
rds= numpy.array(rds)
zhs= numpy.array(zhs)
rdexps= numpy.array(rdexps)
zhexps= numpy.array(zhexps)
dlnvcdlnrs= numpy.array(dlnvcdlnrs)
plhalos= numpy.array(plhalos)
rorss= numpy.array(rorss)
if options.fitdvt:
dvts= numpy.array(dvts)
#derived parameters
surfzs= numpy.array(surfzs)
surfz800s= numpy.array(surfz800s)
surfzdisks= numpy.array(surfzdisks)
massdisks= numpy.array(massdisks)
rhoos= numpy.array(rhoos)
rhooalts= numpy.array(rhooalts)
rhodms= numpy.array(rhodms)
vcdvcros= numpy.array(vcdvcros)
vcdvcs= numpy.array(vcdvcs)
rexps= numpy.sqrt(2.)*(rds+zhs)/2.2
mloglikemins= numpy.array(mloglikemins)
vescs= numpy.array(vescs)
#Load into dictionary
out= {}
out['feh']= fehs
out['afe']= afes
out['zmedian']= zmedians
out['ndata']= ndatas
out['hr']= hrs
out['sr']= srs
out['sz']= szs
out['hsr']= hsrs
out['hsz']= hszs
out['outfrac']= outfracs
out['vc']= vcs
out['rd']= rds
out['zh']= zhs
out['rdexp']= rdexps
out['zhexp']= zhexps
out['dlnvcdlnr']= dlnvcdlnrs
out['plhalo']= plhalos
out['rors']= rorss
out['surfz']= surfzs
out['surfz800']= surfz800s
out['surfzdisk']= surfzdisks
out['massdisk']= massdisks
out['rhoo']= rhoos
out['rhooalt']= rhooalts
out['rhodm']= rhodms
out['vcdvc']= vcdvcs
out['vcdvcro']= vcdvcros
out['rexp']= rexps
out['mloglikemin']= mloglikemins
out['vesc']= vescs
if options.fitdvt:
out['dvt']= dvts
if nomedian: return out
else: return add_median(out,boot=boot)
