def plotbestpot(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()
plotthis_rd= numpy.zeros(npops)+numpy.nan
plotthis_fh= numpy.zeros(npops)+numpy.nan
for ii in range(npops):
sys.stdout.write('\r'+"Working on bin %i / %i ..." % (ii+1,npops))
sys.stdout.flush()
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
rds= numpy.linspace(1.5,4.5,logl.shape[0])
fhs= numpy.linspace(0.,1.,logl.shape[3])
if options.restrictdvt:
logl= logl[:,:,:,:,:,:,:,1:4,:,:,:]
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)
if options.restrictdf:
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,:,:,:,:]
#Find best-fit potential
maxindx= numpy.unravel_index(numpy.argmax(logl),(logl.shape))
plotthis_rd[ii]= rds[maxindx[0]]
plotthis_fh[ii]= fhs[maxindx[3]]
sys.stdout.write('\r'+_ERASESTR+'\r')
sys.stdout.flush()
#Now plot
bovy_plot.bovy_print()
monoAbundanceMW.plotPixelFunc(fehs,afes,plotthis_rd,
vmin=1.5,vmax=4.5,
zlabel=r'$R_d\ [\mathrm{kpc}]$')
#Plotname
spl= options.outfilename.split('.')
newname= ''
for jj in range(len(spl)-1):
newname+= spl[jj]
if not jj == len(spl)-2: newname+= '.'
newname+= '_rd.'
newname+= spl[-1]
bovy_plot.bovy_end_print(newname)
#fh
bovy_plot.bovy_print()
monoAbundanceMW.plotPixelFunc(fehs,afes,plotthis_fh,
vmin=0.,vmax=1.,
zlabel=r'$f_h$')
#Plotname
spl= options.outfilename.split('.')
newname= ''
for jj in range(len(spl)-1):
newname+= spl[jj]
if not jj == len(spl)-2: newname+= '.'
newname+= '_fh.'
newname+= spl[-1]
bovy_plot.bovy_end_print(newname)
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 plotDF4fidpot(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()
plotthis= numpy.zeros(npops)+numpy.nan
for ii in range(npops):
sys.stdout.write('\r'+"Working on bin %i / %i ..." % (ii+1,npops))
sys.stdout.flush()
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
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)
if options.restrictdvt:
logl= logl[:,:,:,:,:,:,:,1:4,:,:,:]
if options.restrictdf:
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,:,:,:,:]
#Find best-fit df for the fiducial potential
indx= numpy.unravel_index(options.index,(logl.shape[0],logl.shape[3]))
logl= logl[indx[0],0,0,indx[1],:,:,:,:,:,:,:]
maxindx= numpy.unravel_index(numpy.argmax(logl),(logl.shape))
if options.subtype.lower() == 'hr':
plotthis[ii]= numpy.exp(hrs[maxindx[0]])*_REFR0
elif options.subtype.lower() == 'sr':
plotthis[ii]= numpy.exp(srs[maxindx[1]])*_REFV0
elif options.subtype.lower() == 'sz':
plotthis[ii]= numpy.exp(szs[maxindx[2]])*_REFV0
sys.stdout.write('\r'+_ERASESTR+'\r')
sys.stdout.flush()
#Now plot
if options.subtype.lower() == 'hr':
vmin, vmax= 1.35,4.5
zlabel=r'$\mathrm{Input\ radial\ scale\ length\ [kpc]}$'
elif options.subtype.lower() == 'sr':
vmin, vmax= 30., 60.
zlabel= r'$\mathrm{Input\ radial\ velocity\ dispersion\ [km\,s}^{-1}]$'
elif options.subtype.lower() == 'sz':
vmin, vmax= 10., 80.
zlabel= r'$\mathrm{Input\ vertical\ velocity\ dispersion\ [km\,s}^{-1}]$'
bovy_plot.bovy_print()
monoAbundanceMW.plotPixelFunc(fehs,afes,plotthis,
vmin=vmin,vmax=vmax,
zlabel=zlabel)
bovy_plot.bovy_end_print(options.outfilename)
def plotQdfPreviousFits(options,args):
#Get 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()
#Run through them and get the apprimxate results
lnhrin= numpy.empty(len(fehs))
lnsrin= numpy.empty(len(fehs))
lnszin= numpy.empty(len(fehs))
for ii in range(len(fehs)):
out= approxFitResult(fehs[ii],afes[ii])
lnhrin[ii]= out[0]
lnsrin[ii]= out[1]
lnszin[ii]= out[2]
#Now plot: hR
bovy_plot.bovy_print(text_fontsize=20.,
legend_fontsize=24.,
xtick_labelsize=18.,
ytick_labelsize=18.,
axes_labelsize=20.)
monoAbundanceMW.plotPixelFunc(fehs,afes,numpy.exp(lnhrin)*8.,
vmin=1.5,vmax=4.86,
zlabel=r'$h^{\mathrm{in}}_R\ (\mathrm{kpc})$')
#Plotname
spl= options.outfilename.split('.')
newname= ''
for jj in range(len(spl)-1):
newname+= spl[jj]
if not jj == len(spl)-2: newname+= '.'
newname+= '_hrin.'
newname+= spl[-1]
bovy_plot.bovy_end_print(newname)
#Now plot: sR
bovy_plot.bovy_print(text_fontsize=20.,
legend_fontsize=24.,
xtick_labelsize=18.,
ytick_labelsize=18.,
axes_labelsize=20.)
monoAbundanceMW.plotPixelFunc(fehs,afes,numpy.exp(lnsrin)*220.,
vmin=30.,vmax=60.,
zlabel=r'$\sigma^{\mathrm{in}}_R\ (\mathrm{km\,s}^{-1})$')
#Plotname
spl= options.outfilename.split('.')
newname= ''
for jj in range(len(spl)-1):
newname+= spl[jj]
if not jj == len(spl)-2: newname+= '.'
newname+= '_srin.'
newname+= spl[-1]
bovy_plot.bovy_end_print(newname)
#Now plot: sZ
bovy_plot.bovy_print(text_fontsize=20.,
legend_fontsize=24.,
xtick_labelsize=18.,
ytick_labelsize=18.,
axes_labelsize=20.)
monoAbundanceMW.plotPixelFunc(fehs,afes,numpy.exp(lnszin)*220.,
vmin=10.,vmax=80.,
zlabel=r'$\sigma^{\mathrm{in}}_Z\ (\mathrm{km\,s}^{-1})$')
#Plotname
spl= options.outfilename.split('.')
newname= ''
for jj in range(len(spl)-1):
newname+= spl[jj]
if not jj == len(spl)-2: newname+= '.'
newname+= '_szin.'
newname+= spl[-1]
bovy_plot.bovy_end_print(newname)
