<jupyter_start><jupyter_text>Radial dependence<jupyter_code>betw = lambda x,l,u: (x>l)&(x<u)
rranges = [[3,5],[7,9],[11,13]]
selectionR = [
        (apogee['FE_H_ERR']<0.2)&(apogee['LOGG']<3.5)&betw(apogee['GALR'],rd,ru)&(np.abs(apogee['GALZ'])>0.5) for rd,ru in rranges
]

f,a=plt.subplots(1,3,figsize=[15.,3.],sharex=True,sharey=True)
e='O'
for rr in range(3):
    plt.sca(a[rr])
    plt.plot(flM.abund['Fe'][ssM[rr]]-flM.abund['H'][ssM[rr]],
                 (flM.abund[e]-flM.abund['Fe'])[ssM[rr]]+0.2,c='b',lw=3)
    plt.hist2d(apogee['FE_H'][selectionR[rr]],
               (apogee['%s_H'%e.upper()]-apogee['FE_H'])[selectionR[rr]],
              bins=50,range=[[-1.5,0.6],[-0.1,0.4]],norm=LogNorm(),cmap=plt.cm.Spectral_r);
    plt.xlabel(r'$\mathrm{[Fe/H]}$')
    if rr==0:
        plt.ylabel(r'$\mathrm{[%s/Fe]}$'%e)
    plt.title('$%i<R/\mathrm{kpc}<%i$'%(rranges[rr][0],rranges[rr][1]),fontsize=15)
plt.plot(flM.Mgas.T[ssM[1]]**1.3,flM.SFR);
# plt.semilogy()
# plt.semilogx()
xx = np.linspace(100.,300.)
plt.plot(xx,xx/10.)
plt.plot(flM.t,flM.Inflow)
flM.abund
ages = np.linspace(1.,8.)
T = 13.7-ages
plt.plot(T,np.array(flM.paint(8.,T,['Fe'])).T);
plt.plot(flM.t,flM.Z.T[ssM[1]])
fepredict=flM.paint(apogee['rl'], apogee['age'],['Fe','O'])
plt.hist(fepredict['O_H']-fepredict['Fe_H'],bins=50,histtype='step',range=[-0.4,0.5]);
plt.hist(apogee['O_H']-apogee['FE_H'],bins=50,histtype='step',range=[-0.4,0.5]);
plt.plot(flM.SFR,flM.Mgas.T[ssM[1]]**1.4);
plt.semilogy()<jupyter_output><empty_output>
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