def plot_Xscale_3D(self, ax, gp): rmin = np.log10(min(gp.xipol)) rmax = np.log10(max(gp.xipol)) gp.xfine = np.logspace(rmin, rmax, gp.nfine) if gp.investigate == 'walk': if gp.pops == 1: rhodm, nu1 = ga.rho_walk(gp.xepol, gp) else: rhodm, nu1, nu2 = ga.rho_walk(gp.xepol, gp) elif gp.investigate == 'gaia': rhodm, nu1 = ga.rho_gaia(gp.xepol, gp) for pop in range(gp.pops): # use our models nuprof = self.Mmedi.get_prof('nu', pop+1) tck = splrep(gp.xepol, nuprof) nuproffine = splev(gp.xfine, tck) if gp.investigate == 'walk' or gp.investigate == 'gaia': # or rather use analytic values, where available if pop == 0: nuprof = nu1 elif pop == 1: nuprof = nu2 if gp.geom == 'sphere': Mprof = gip.rho_SUM_Mr(gp.xfine, nuproffine) Mmax = max(Mprof) # Mprof[-1] ihalf = -1 for kk in range(len(Mprof)): # half-light radius (3D) is where mass is more than half # ihalf gives the iindex of where this happens if Mprof[kk] >= Mmax/2 and ihalf < 0: xx = (gp.xfine[kk-1]+gp.xfine[kk])/2 print('rhalf = ', xx, ' pc') ax.axvline(xx, color='green', lw=0.5, alpha=0.7) ihalf = kk
def geom_loglike(cube, ndim, nparams, gp): tmp_profs = Profiles(gp.pops, gp.nepol) off = 0 offstep = gp.nrho if gp.chi2_Sig_converged <= 0: rhodmpar = np.array(cube[off : off + offstep]) tmp_rho0 = phys.rho(gp.xepol, rhodmpar, 0, gp) # for J factor calculation (has been deferred to output routine) # tmp_rhofine = phys.rho(gp.xfine, rhodmpar, 0, gp) # tmp_Jfine = gip.Jpar(gp.xfine, tmp_rhofine, gp) #tmp_rhofine # tck = splrep(gp.xfine[:-3], tmp_Jfine) # tmp_J = splev(gp.xepol, tck) # rhodmpar hold [rho(rhalf), nr to be used for integration # from halflight radius, defined on gp.xepol] # (only calculate) M, check tmp_M0 = gip.rho_SUM_Mr(gp.xepol, tmp_rho0) # store profiles tmp_profs.set_prof("nr", 1.0 * rhodmpar[1 + 1 : -1], 0, gp) tmp_profs.set_prof("rho", tmp_rho0, 0, gp) # tmp_profs.set_prof('J', tmp_J, 0, gp) tmp_profs.set_prof("M", tmp_M0, 0, gp) off += offstep # anyhow, even if Sig not yet converged # get profile for rho* if gp.investigate == "obs": offstep = gp.nrho lbaryonpar = np.array(cube[off : off + offstep]) rhostar = phys.rho(gp.xepol, lbaryonpar, 0, gp) off += offstep Signu = gip.rho_param_INT_Sig(gp.xepol, lbaryonpar, 0, gp) # [Munit/pc^2] MtoL = cube[off] off += 1 # store these profiles every time tmp_profs.set_prof("nu", rhostar, 0, gp) tmp_profs.set_prof("Sig", Signu, 0, gp) tmp_profs.set_MtoL(MtoL) else: lbaryonpar = np.zeros(gp.nrho) MtoL = 0.0 for pop in np.arange(1, gp.pops + 1): # [1, 2, ..., gp.pops] offstep = gp.nrho nupar = np.array(cube[off : off + offstep]) tmp_nrnu = 1.0 * nupar[1 + 1 : -1] tmp_nu = phys.rho(gp.xepol, nupar, pop, gp) tmp_Signu = gip.rho_param_INT_Sig(gp.xepol, nupar, pop, gp) # tmp_nu = pool.apply_async(phys.rho, [gp.xepol, nupar, pop, gp]) # tmp_Signu = pool.apply_async(gip.rho_param_INT_Sig, [gp.xepol, nupar, pop, gp]) off += offstep offstep = 1 tmp_hyperSig = cube[off : off + offstep] off += offstep offstep = 1 tmp_hypersig = cube[off : off + offstep] off += offstep offstep = gp.nbeta if gp.chi2_Sig_converged <= 0: betapar = np.array(cube[off : off + offstep]) tmp_beta, tmp_betastar = phys.beta(gp.xepol, betapar, gp) if check_beta(tmp_beta, gp): gh.LOG(2, "beta error") tmp_profs.chi2 = gh.err(1.0, gp) return tmp_profs try: # if True: if gp.checksig and gp.investigate == "hern": import gi_analytic as ga anrho = ga.rho(gp.xepol, gp)[0] rhodmpar_half = np.exp(splev(gp.dat.rhalf[0], splrep(gp.xepol, np.log(anrho)))) nr = -gh.derivipol(np.log(anrho), np.log(gp.xepol)) dlr = np.hstack([nr[0], nr, nr[-1]]) if gp.investigate == "gaia": dlr[-1] = 4 rhodmpar = np.hstack([rhodmpar_half, dlr]) lbaryonpar = 0.0 * rhodmpar MtoL = 0.0 betapar = np.array([0, 0, 2, max(gp.xipol) / 2]) # for hern annu = ga.rho(gp.xepol, gp)[1] nupar_half = np.exp(splev(gp.dat.rhalf[1], splrep(gp.xepol, np.log(annu)))) nrnu = -gh.derivipol(np.log(annu), np.log(gp.xepol)) dlrnu = np.hstack([nrnu[0], nrnu, nrnu[-1]]) if gp.investigate == "gaia": dlrnu[-1] = 6 nupar = np.hstack([nupar_half, dlrnu]) elif gp.checkbeta and gp.investigate == "gaia": # rhodmpar = np.array([ 0.41586608, 0.38655515, 0.60898657, 0.50936769, 0.52601378, 0.54526758, 0.5755599, 0.57900806, 0.60252357, 0.60668445, 0.62252721, 0.63173754, 0.64555439, 0.65777175, 0.67083556, 0.68506606, 0.69139872, 0.66304763, 0.61462276, 0.70916575, 0.53287872]) rhodmpar = np.array( [ 0.18235821, 0.4719348, 0.0, 0.0, 0.10029569, 0.11309553, 0.25637863, 0.31815175, 0.40621336, 0.46247927, 0.53545415, 0.60874961, 0.68978141, 0.79781574, 0.91218048, 1.08482356, 1.36074895, 1.88041885, 2.31792908, 2.62089078, 3.001, ] ) betapar = np.array([1.23555034e-03, 9.89999994e-01, 2.03722518e00, 5.85640906e00]) nupar = np.array( [ 0.15649498, 6.65618254, 0.10293663, 0.1087109, 0.13849277, 0.24371261, 0.62633345, 1.05913181, 1.43774113, 1.82346043, 2.20091446, 2.60007997, 2.98745825, 3.423104, 3.80766658, 4.2089698, 4.62950843, 4.91166037, 4.97380638, 4.99718073, 5.2277589, ] ) gp.dat.nrnu = [ np.array( [ 0.15476906, 0.85086798, 0.9342867, 0.88161169, 0.83254241, 0.85086798, 0.99930431, 1.22211638, 1.47184763, 1.78910057, 2.1987677, 2.51961046, 2.80345393, 3.10336133, 3.88504346, 4.52442727, 4.88817769, 5.07880404, 4.83455511, 6.32165657, 4.88817769, ] ), np.array( [ 0.15476906, 0.85086798, 0.9342867, 0.88161169, 0.83254241, 0.85086798, 0.99930431, 1.22211638, 1.47184763, 1.78910057, 2.1987677, 2.51961046, 2.80345393, 3.10336133, 3.88504346, 4.52442727, 4.88817769, 5.07880404, 4.83455511, 6.32165657, 4.88817769, ] ), np.array( [ 0.15476906, 0.85086798, 0.9342867, 0.88161169, 0.83254241, 0.85086798, 0.99930431, 1.22211638, 1.47184763, 1.78910057, 2.1987677, 2.51961046, 2.80345393, 3.10336133, 3.88504346, 4.52442727, 4.88817769, 5.07880404, 4.83455511, 6.32165657, 4.88817769, ] ), np.array( [ 0.15476906, 0.85086798, 0.9342867, 0.88161169, 0.83254241, 0.85086798, 0.99930431, 1.22211638, 1.47184763, 1.78910057, 2.1987677, 2.51961046, 2.80345393, 3.10336133, 3.88504346, 4.52442727, 4.88817769, 5.07880404, 4.83455511, 6.32165657, 4.88817769, ] ), ] gp.dat.nrnuerr = [ np.array( [ 0.05158969, 12.22044422, 2.44408884, 2.44408884, 2.44408884, 2.44408884, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 2.44408884, 2.44408884, 2.44408884, 2.44408884, ] ), np.array( [ 0.05158969, 12.22044422, 2.44408884, 2.44408884, 2.44408884, 2.44408884, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 2.44408884, 2.44408884, 2.44408884, 2.44408884, ] ), np.array( [ 0.05158969, 12.22044422, 2.44408884, 2.44408884, 2.44408884, 2.44408884, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 2.44408884, 2.44408884, 2.44408884, 2.44408884, ] ), np.array( [ 0.05158969, 12.22044422, 2.44408884, 2.44408884, 2.44408884, 2.44408884, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 2.44408884, 2.44408884, 2.44408884, 2.44408884, ] ), ] lbaryonpar = 0.0 * rhodmpar MtoL = 0.0 sig, kap, zetaa, zetab = phys.sig_kap_zet(gp.xepol, rhodmpar, lbaryonpar, MtoL, nupar, betapar, pop, gp) # fill_between(gp.xipol, gp.dat.sig[1]-gp.dat.sigerr[1], gp.dat.sig[1]+gp.dat.sigerr[1]) # plot(gp.xepol, sig, 'r') # xscale('log') # ylim([0, 30]) # xlabel('$r$ [pc]') # ylabel('$\sigma_{LOS}$ [km/s]') # savefig('siglos_gaia_2.pdf') # pdb.set_trace() except Exception: gh.LOG(1, "sigma error") tmp_profs.chi2 = gh.err(2.0, gp) return tmp_profs # now store the profiles gh.sanitize_vector(tmp_beta, len(tmp_profs.x0), -200, 1, gp.debug) tmp_profs.set_prof("beta", tmp_beta, pop, gp) gh.sanitize_vector(tmp_betastar, len(tmp_profs.x0), -1, 1, gp.debug) tmp_profs.set_prof("betastar", tmp_betastar, pop, gp) tmp_profs.set_prof("sig", sig, pop, gp) tmp_profs.hypersig = tmp_hypersig tmp_profs.set_prof("kap", kap, pop, gp) tmp_profs.set_zeta(zetaa, zetab, pop) tmp_profs.set_prof("nrnu", tmp_nrnu, pop, gp) tmp_profs.set_prof("nu", tmp_nu, pop, gp) # pool: tmp_nu.get() # following profile needs to be stored at all times, to calculate chi tmp_profs.set_prof("Sig", tmp_Signu, pop, gp) tmp_profs.hyperSig = tmp_hyperSig off += offstep # still do this even if gp.chi2_Sig_converged is False if off != gp.ndim: gh.LOG(1, "wrong subscripts in gi_loglike") pdb.set_trace() # determine log likelihood chi2 = calc_chi2(tmp_profs, gp) gh.LOG(-1, gp.investigate + "/" + str(gp.case) + "/" + gp.files.timestamp + ": ln L = ", gh.pretty(-chi2 / 2.0)) # x=gp.dat.rbin # linedat,=ax.loglog(x, gp.dat.Sig[1], 'b') # line,=ax.loglog(x, tmp_profs.get_prof("Sig", 1), 'r', alpha=0.1) # plt.draw() # plt.show() tmp_profs.chi2 = chi2 # after some predefined wallclock time and Sig convergence, plot all profiles # if time.time() - gp.last_plot >= gp.plot_after and gp.chi2_Sig_converged <= 0: # gp.last_plot = time.time() # try: # import plotting.plot_profiles # plotting.plot_profiles.run(gp.files.timestamp, gp.files.outdir, gp) # except: # print('plotting error in gi_loglike!') # close pool automatically after with clause return tmp_profs
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 geom_loglike(cube, ndim, nparams, gp): tmp_profs = Profiles(gp.pops, gp.nepol) off = 0 offstep = gp.nrho if gp.chi2_Sig_converged <= 0: rhodmpar = np.array(cube[off:off + offstep]) tmp_rho0 = phys.rho(gp.xepol, rhodmpar, 0, gp) # for J factor calculation (has been deferred to output routine) #tmp_rhofine = phys.rho(gp.xfine, rhodmpar, 0, gp) #tmp_Jfine = gip.Jpar(gp.xfine, tmp_rhofine, gp) #tmp_rhofine #tck = splrep(gp.xfine[:-3], tmp_Jfine) #tmp_J = splev(gp.xepol, tck) # rhodmpar hold [rho(rhalf), nr to be used for integration # from halflight radius, defined on gp.xepol] # (only calculate) M, check tmp_M0 = gip.rho_SUM_Mr(gp.xepol, tmp_rho0) # store profiles tmp_profs.set_prof('nr', 1. * rhodmpar[1 + 1:-1], 0, gp) tmp_profs.set_prof('rho', tmp_rho0, 0, gp) #tmp_profs.set_prof('J', tmp_J, 0, gp) tmp_profs.set_prof('M', tmp_M0, 0, gp) off += offstep # anyhow, even if Sig not yet converged # get profile for rho* if gp.investigate == 'obs': offstep = gp.nrho lbaryonpar = np.array(cube[off:off + offstep]) rhostar = phys.rho(gp.xepol, lbaryonpar, 0, gp) off += offstep Signu = gip.rho_param_INT_Sig(gp.xepol, lbaryonpar, 0, gp) # [Munit/pc^2] MtoL = cube[off] off += 1 # store these profiles every time tmp_profs.set_prof('nu', rhostar, 0, gp) tmp_profs.set_prof('Sig', Signu, 0, gp) tmp_profs.set_MtoL(MtoL) else: lbaryonpar = np.zeros(gp.nrho) MtoL = 0. for pop in np.arange(1, gp.pops + 1): # [1, 2, ..., gp.pops] offstep = gp.nrho nupar = np.array(cube[off:off + offstep]) tmp_nrnu = 1. * nupar[1 + 1:-1] tmp_nu = phys.rho(gp.xepol, nupar, pop, gp) tmp_Signu = gip.rho_param_INT_Sig(gp.xepol, nupar, pop, gp) #tmp_nu = pool.apply_async(phys.rho, [gp.xepol, nupar, pop, gp]) #tmp_Signu = pool.apply_async(gip.rho_param_INT_Sig, [gp.xepol, nupar, pop, gp]) off += offstep offstep = 1 tmp_hyperSig = cube[off:off + offstep] off += offstep offstep = 1 tmp_hypersig = cube[off:off + offstep] off += offstep offstep = gp.nbeta if gp.chi2_Sig_converged <= 0: betapar = np.array(cube[off:off + offstep]) tmp_beta, tmp_betastar = phys.beta(gp.xepol, betapar, gp) if check_beta(tmp_beta, gp): gh.LOG(2, 'beta error') tmp_profs.chi2 = gh.err(1., gp) return tmp_profs try: #if True: if gp.checksig and gp.investigate == 'hern': import gi_analytic as ga anrho = ga.rho(gp.xepol, gp)[0] rhodmpar_half = np.exp( splev(gp.dat.rhalf[0], splrep(gp.xepol, np.log(anrho)))) nr = -gh.derivipol(np.log(anrho), np.log(gp.xepol)) dlr = np.hstack([nr[0], nr, nr[-1]]) if gp.investigate == 'gaia': dlr[-1] = 4 rhodmpar = np.hstack([rhodmpar_half, dlr]) lbaryonpar = 0.0 * rhodmpar MtoL = 0.0 betapar = np.array([0, 0, 2, max(gp.xipol) / 2]) # for hern annu = ga.rho(gp.xepol, gp)[1] nupar_half = np.exp( splev(gp.dat.rhalf[1], splrep(gp.xepol, np.log(annu)))) nrnu = -gh.derivipol(np.log(annu), np.log(gp.xepol)) dlrnu = np.hstack([nrnu[0], nrnu, nrnu[-1]]) if gp.investigate == 'gaia': dlrnu[-1] = 6 nupar = np.hstack([nupar_half, dlrnu]) elif gp.checkbeta and gp.investigate == 'gaia': # rhodmpar = np.array([ 0.41586608, 0.38655515, 0.60898657, 0.50936769, 0.52601378, 0.54526758, 0.5755599, 0.57900806, 0.60252357, 0.60668445, 0.62252721, 0.63173754, 0.64555439, 0.65777175, 0.67083556, 0.68506606, 0.69139872, 0.66304763, 0.61462276, 0.70916575, 0.53287872]) rhodmpar = np.array([ 0.18235821, 0.4719348, 0., 0., 0.10029569, 0.11309553, 0.25637863, 0.31815175, 0.40621336, 0.46247927, 0.53545415, 0.60874961, 0.68978141, 0.79781574, 0.91218048, 1.08482356, 1.36074895, 1.88041885, 2.31792908, 2.62089078, 3.001 ]) betapar = np.array([ 1.23555034e-03, 9.89999994e-01, 2.03722518e+00, 5.85640906e+00 ]) nupar = np.array([ 0.15649498, 6.65618254, 0.10293663, 0.1087109, 0.13849277, 0.24371261, 0.62633345, 1.05913181, 1.43774113, 1.82346043, 2.20091446, 2.60007997, 2.98745825, 3.423104, 3.80766658, 4.2089698, 4.62950843, 4.91166037, 4.97380638, 4.99718073, 5.2277589 ]) gp.dat.nrnu = [ np.array([ 0.15476906, 0.85086798, 0.9342867, 0.88161169, 0.83254241, 0.85086798, 0.99930431, 1.22211638, 1.47184763, 1.78910057, 2.1987677, 2.51961046, 2.80345393, 3.10336133, 3.88504346, 4.52442727, 4.88817769, 5.07880404, 4.83455511, 6.32165657, 4.88817769 ]), np.array([ 0.15476906, 0.85086798, 0.9342867, 0.88161169, 0.83254241, 0.85086798, 0.99930431, 1.22211638, 1.47184763, 1.78910057, 2.1987677, 2.51961046, 2.80345393, 3.10336133, 3.88504346, 4.52442727, 4.88817769, 5.07880404, 4.83455511, 6.32165657, 4.88817769 ]), np.array([ 0.15476906, 0.85086798, 0.9342867, 0.88161169, 0.83254241, 0.85086798, 0.99930431, 1.22211638, 1.47184763, 1.78910057, 2.1987677, 2.51961046, 2.80345393, 3.10336133, 3.88504346, 4.52442727, 4.88817769, 5.07880404, 4.83455511, 6.32165657, 4.88817769 ]), np.array([ 0.15476906, 0.85086798, 0.9342867, 0.88161169, 0.83254241, 0.85086798, 0.99930431, 1.22211638, 1.47184763, 1.78910057, 2.1987677, 2.51961046, 2.80345393, 3.10336133, 3.88504346, 4.52442727, 4.88817769, 5.07880404, 4.83455511, 6.32165657, 4.88817769 ]) ] gp.dat.nrnuerr = [ np.array([ 0.05158969, 12.22044422, 2.44408884, 2.44408884, 2.44408884, 2.44408884, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 2.44408884, 2.44408884, 2.44408884, 2.44408884 ]), np.array([ 0.05158969, 12.22044422, 2.44408884, 2.44408884, 2.44408884, 2.44408884, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 2.44408884, 2.44408884, 2.44408884, 2.44408884 ]), np.array([ 0.05158969, 12.22044422, 2.44408884, 2.44408884, 2.44408884, 2.44408884, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 2.44408884, 2.44408884, 2.44408884, 2.44408884 ]), np.array([ 0.05158969, 12.22044422, 2.44408884, 2.44408884, 2.44408884, 2.44408884, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 0.48881777, 2.44408884, 2.44408884, 2.44408884, 2.44408884 ]) ] lbaryonpar = 0.0 * rhodmpar MtoL = 0.0 sig, kap, zetaa, zetab = phys.sig_kap_zet( gp.xepol, rhodmpar, lbaryonpar, MtoL, nupar, betapar, pop, gp) #fill_between(gp.xipol, gp.dat.sig[1]-gp.dat.sigerr[1], gp.dat.sig[1]+gp.dat.sigerr[1]) #plot(gp.xepol, sig, 'r') #xscale('log') #ylim([0, 30]) #xlabel('$r$ [pc]') #ylabel('$\sigma_{LOS}$ [km/s]') #savefig('siglos_gaia_2.pdf') #pdb.set_trace() except Exception: gh.LOG(1, 'sigma error') tmp_profs.chi2 = gh.err(2., gp) return tmp_profs # now store the profiles gh.sanitize_vector(tmp_beta, len(tmp_profs.x0), -200, 1, gp.debug) tmp_profs.set_prof('beta', tmp_beta, pop, gp) gh.sanitize_vector(tmp_betastar, len(tmp_profs.x0), -1, 1, gp.debug) tmp_profs.set_prof('betastar', tmp_betastar, pop, gp) tmp_profs.set_prof('sig', sig, pop, gp) tmp_profs.hypersig = tmp_hypersig tmp_profs.set_prof('kap', kap, pop, gp) tmp_profs.set_zeta(zetaa, zetab, pop) tmp_profs.set_prof('nrnu', tmp_nrnu, pop, gp) tmp_profs.set_prof('nu', tmp_nu, pop, gp) # pool: tmp_nu.get() # following profile needs to be stored at all times, to calculate chi tmp_profs.set_prof('Sig', tmp_Signu, pop, gp) tmp_profs.hyperSig = tmp_hyperSig off += offstep # still do this even if gp.chi2_Sig_converged is False if off != gp.ndim: gh.LOG(1, 'wrong subscripts in gi_loglike') pdb.set_trace() # determine log likelihood chi2 = calc_chi2(tmp_profs, gp) gh.LOG( -1, gp.investigate + '/' + str(gp.case) + '/' + gp.files.timestamp + ': ln L = ', gh.pretty(-chi2 / 2.)) # x=gp.dat.rbin # linedat,=ax.loglog(x, gp.dat.Sig[1], 'b') # line,=ax.loglog(x, tmp_profs.get_prof("Sig", 1), 'r', alpha=0.1) # plt.draw() # plt.show() tmp_profs.chi2 = chi2 # after some predefined wallclock time and Sig convergence, plot all profiles #if time.time() - gp.last_plot >= gp.plot_after and gp.chi2_Sig_converged <= 0: # gp.last_plot = time.time() # try: # import plotting.plot_profiles # plotting.plot_profiles.run(gp.files.timestamp, gp.files.outdir, gp) # except: # print('plotting error in gi_loglike!') # close pool automatically after with clause return tmp_profs