def plot_hzszq(options,args):
"""Plot sz,hz, q"""
if len(args) == 0.:
print "Must provide a savefilename ..."
print "Returning ..."
return None
nqs, nszs, nhzs= 31, 51, 51
#nqs, nszs, nhzs= 5,5,5
if os.path.exists(args[0]):
#Load
savefile= open(args[0],'rb')
hzs= pickle.load(savefile)
szs= pickle.load(savefile)
qs= pickle.load(savefile)
savefile.close()
else:
qs= numpy.linspace(0.5,1.,nqs)
szs= numpy.linspace(15.,50.,nszs)
hzs= numpy.zeros((nqs,nszs))
for ii in range(nqs):
print "Working on potential %i / %i ..." % (ii+1,nqs)
#Setup potential
lp= LogarithmicHaloPotential(normalize=1.,q=qs[ii])
if options.aAmethod.lower() == 'staeckel':
aA= actionAngleStaeckel(pot=lp,delta=0.45,c=True)
else:
aA=actionAngleAdiabaticGrid(pot=pot,nR=16,nEz=16,nEr=31,nLz=31,
zmax=1.,Rmax=5.)
for jj in range(nszs):
qdf= quasiisothermaldf(options.hr/8.,2.*szs[jj]/220.,
szs[jj]/220.,7./8.,7./8.,pot=lp,
aA=aA,cutcounter=True)
hzs[ii,jj]= qdf.estimate_hz(1.,z=0.125)
#Save
save_pickles(args[0],hzs,szs,qs)
#Re-sample
hzsgrid= numpy.linspace(50.,1500.,nhzs)/8000.
qs2d= numpy.zeros((nhzs,nszs))
for ii in range(nszs):
interpQ= interpolate.UnivariateSpline(hzs[:,ii],qs,k=3)
qs2d[:,ii]= interpQ(hzsgrid)
qs2d[(hzsgrid < hzs[0,ii]),ii]= numpy.nan
qs2d[(hzsgrid > hzs[-1,ii]),ii]= numpy.nan
#Now plot
bovy_plot.bovy_print(fig_width=6.,
text_fontsize=20.,
legend_fontsize=24.,
xtick_labelsize=18.,
ytick_labelsize=18.,
axes_labelsize=24.)
bovy_plot.bovy_dens2d(qs2d.T,origin='lower',cmap='jet',
interpolation='gaussian',
# interpolation='nearest',
ylabel=r'$\sigma_z\ [\mathrm{km\,s}^{-1}]$',
xlabel=r'$h_z\ [\mathrm{pc}]$',
zlabel=r'$\mathrm{flattening}\ q$',
yrange=[szs[0],szs[-1]],
xrange=[8000.*hzsgrid[0],8000.*hzsgrid[-1]],
# vmin=0.5,vmax=1.,
contours=False,
colorbar=True,shrink=0.78)
_OVERPLOTMAPS= True
if _OVERPLOTMAPS:
fehs= monoAbundanceMW.fehs()
afes= monoAbundanceMW.afes()
npops= len(fehs)
mapszs= []
maphzs= []
for ii in range(npops):
thissz, thiserr= monoAbundanceMW.sigmaz(fehs[ii],afes[ii],err=True)
if thiserr/thissz > 0.1:
continue
thishz, thiserr= monoAbundanceMW.hz(fehs[ii],afes[ii],err=True)
if thiserr/thishz > 0.1:
continue
mapszs.append(thissz)
maphzs.append(thishz)
mapszs= numpy.array(mapszs)
maphzs= numpy.array(maphzs)
bovy_plot.bovy_plot(maphzs,mapszs,'ko',overplot=True,mfc='none',mew=1.5)
bovy_plot.bovy_text(r'$h_R = %i\,\mathrm{kpc}$' % int(options.hr),
bottom_right=True)
bovy_plot.bovy_end_print(options.plotfilename)
import monoAbundanceMW as mam
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')
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
