def lum_err_compare(newL_err,L_err):
figure()
hist(log10(newL_err),bins=20,label=utils.texstr('Corrected'),histtype='step',lw=3,ec='r')
hist(log10(L_err),bins=20,label=utils.texstr('Original'),histtype='step',lw=3,ec='k')
legend(loc=0)
xlabel(r'$log(\Delta L/L_{\odot})$')
ylabel(r'$\rm{Count}$')
def lum_compare(newL,lum_raw):
figure()
print 'min(newL), max(newL) = ', ma.min(newL,axis=0), ma.max(newL,axis=0)
#lmsk = where(log10(lum_raw)>4)[0] # To temporarily mask bad L to see the distbn of just the good values
#lum_raw, newL = lum_raw[lmsk], newL[lmsk]
hist(log10(newL),bins=20,label=utils.texstr('Corrected'),histtype='step',lw=3,ec='r')
hist(log10(lum_raw),bins=20,label=utils.texstr('Original'),histtype='step',lw=3,ec='k')
legend(loc=0,frameon=False)
xlabel(r'$\log(L/(L_{\odot}\,h^{-2}))$')
ylabel(r'$\rm{Count}$')