def analytic_betastar(x):
ba = ga.beta_gaia(x, gp)[0]
ba = phys.beta2betastar(ba)
return ba
def set_analytic(self, x0, gp):
r0 = x0 # [pc], spherical case
self.analytic.x0 = r0
anbeta = []; annu = []; anSig = []
if gp.investigate == 'gaia':
anrho = ga.rho_gaia(r0, gp)[0]
anM = gip.rho_SUM_Mr(r0, anrho)
annr = ga.nr3Dtot_gaia(r0, gp)
tmp_annu = ga.rho_gaia(r0, gp)[1]
annu.append( tmp_annu )
anSig.append( gip.rho_INT_Sig(r0, tmp_annu, gp) )
for pop in np.arange(1, gp.pops+1):
beta = ga.beta_gaia(r0, gp)[pop]
anbeta.append(beta)
nu = ga.rho_gaia(r0,gp)[pop]
annu.append(nu)
anSig.append(gip.rho_INT_Sig(r0, nu, gp))
elif gp.investigate == 'walk':
anrho = ga.rho_walk(r0, gp)[0]
anM = gip.rho_SUM_Mr(r0, anrho)
annr = ga.nr3Dtot_deriv_walk(r0, gp) # TODO too high in case of core
tmp_annu = ga.rho_walk(r0, gp)[1]
annu.append( tmp_annu )
anSig.append( gip.rho_INT_Sig(r0, tmp_annu, gp) )
for pop in np.arange(1, gp.pops+1):
beta = ga.beta_walk(r0, gp)[pop]
anbeta.append(beta)
nu = ga.rho_walk(r0, gp)[pop]
dum,dum,dum,nudat,nuerr = np.transpose(np.loadtxt(gp.files.nufiles[pop], unpack=False, skiprows=1))
locrhalf = np.argmin(abs(gp.xipol-gp.Xscale[pop]))
nuhalf = nudat[locrhalf]*gp.nu0pc[pop]
annuhalf = nu[np.argmin(abs(r0-locrhalf))]
annu.append(nu*nuhalf/annuhalf)
dum,dum,dum,Sigdat,Sigerr = np.transpose(np.loadtxt(gp.files.Sigfiles[pop], unpack=False, skiprows=1))
locrhalf = np.argmin(abs(gp.xipol-gp.Xscale[pop]))
Sighalf = Sigdat[locrhalf]*gp.Sig0pc[pop]
Sig = gip.rho_INT_Sig(r0, nu, gp)
anSighalf = Sig[np.argmin(abs(r0-locrhalf))]
anSig.append(Sig*Sighalf/anSighalf)
elif gp.investigate == 'triax':
anrho = ga.rho_triax(r0, gp) # one and only
anM = gip.rho_SUM_Mr(r0, anrho)
annr = ga.nr3Dtot_deriv_triax(r0, gp)
tmp_annu = ga.rho_triax(r0, gp) # TODO, M/L=1 assumed here, wrong
annu.append(tmp_annu)
anSig.append( gip.rho_INT_Sig(r0, tmp_annu, gp))
for pop in np.arange(1, gp.pops+1):
beta = ga.beta_triax(r0)
anbeta.append(beta)
nu = ga.rho_triax(r0, gp) # TODO, assumes M/L=1
annu.append(nu)
anSig.append( gip.rho_INT_Sig(r0, nu, gp))
self.analytic.set_prof('rho', anrho, 0, gp)
self.analytic.set_prof('M', anM, 0, gp)
self.analytic.set_prof('nr', annr, 0, gp)
self.analytic.set_prof('nu', annu[0], 0, gp)
self.analytic.set_prof('nrnu', -gh.derivipol(np.log(annu[0]), np.log(r0)), 0, gp)
self.analytic.set_prof('Sig', anSig[0], 0, gp)
for pop in np.arange(1, gp.pops+1):
self.analytic.set_prof('beta', anbeta[pop-1], pop, gp)
self.analytic.set_prof('betastar', anbeta[pop-1]/(2.-anbeta[pop-1]), pop, gp)
self.analytic.set_prof('nu', annu[pop], pop, gp)
nrnu = -gh.derivipol(np.log(annu[pop]), np.log(r0))
self.analytic.set_prof('nrnu', nrnu, pop, gp)
self.analytic.set_prof('Sig', anSig[pop] , pop, gp)#/ Signorm, pop, gp)
self.analytic.set_prof('sig', -np.ones(len(r0)), pop, gp)
return
def analytic_beta(x, gp):
#r_a1 = 1000
# gaia 1 pop
return ga.beta_gaia(x, gp)[0]
