def add_bovy_rix(axScatter,Rmin,Rmax,zmin,zmax):
#Now calculate and plot our model
fehs= numpy.linspace(-1.6,0.5,26)
afes= numpy.linspace(-0.15,0.55,25)
ourDist= numpy.zeros((len(fehs),len(afes)))
for ii in range(len(fehs)):
for jj in range(len(afes)):
ourDist[ii,jj]= maps.abundanceDist(fehs[ii],afes[jj],
z=[zmin*1000.,zmax*1000.],
r=[Rmin,Rmax])
_ADJUSTABUNDANCES= True
_ADJUSTFEH= True
if _ADJUSTABUNDANCES:
if _ADJUSTFEH:
fehs+= 0.15
afes/= 1.5
afes-= 0.025
#Contour this in axScatter
ourDist[numpy.isnan(ourDist)]= 0.
sortindx= numpy.argsort(ourDist.flatten())[::-1]
cumul= numpy.cumsum(numpy.sort(ourDist.flatten())[::-1])/numpy.sum(ourDist.flatten())
cntrThis= numpy.zeros(numpy.prod(ourDist.shape))
cntrThis[sortindx]= cumul
cntrThis= numpy.reshape(cntrThis,ourDist.shape)
pyplot.sca(axScatter)
CS= pyplot.contour(fehs,afes,
cntrThis.T,levels=[0.68,0.95],
linewidths=3.,linestyles='dashed',
colors='r',zorder=10)
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)
plt.ylim(0,2000)
