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
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 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 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 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)
