def calc_M_nu_sig_disc():
import physics_disc as phys
gp.nu1_x = phys.nu(gp.parst.nu1)
# gp.nu1_x = phys.nu_decrease(gp.xipol,gp.xipol,gp.parst.nu1)
gp.d1_x = phys.delta(gp.parst.delta1)
gp.dens_x = phys.dens(gp.xipol, gp.parst.dens)
# means Kz, *not* surface density
# gp.dens_x = gp.parst.dens+gp.blow # Kzpars with baryonic lower limit
gp.M_x = phys.Sigmaz(gp.dens_x)
# gp.M_x = phys.kz(gp.xipol, gp.xipol, gp.dens_x, gp.blow)
# from kz, added baryons min
# TODO: marginalise over lower bound, see gl_priors, kappa_DM < 0
# attention: gives sometimes unphysical decreasing surface density
gp.blow = gp.dat.Mdat # npr.normal(gp.dat.Mdat,gp.dat.Merr/4.,gp.nipol)
# TODO: naming
Rsun = 8000.; hr = 3.0; hsig = 3.0
gp.sig1_x = phys.sigmaz(gp.xipol, gp.dens_x, gp.nu1_x, gp.parst.norm1,\
gp.parst.delta1, [Rsun,hr,hsig])
gp.kap1_x = gp.xipol*0. # set to 0 consistently
if gp.pops == 2:
gp.nu2_x = phys.nu(gp.parst.nu2)
# gp.nu2_x = phys.nu_decrease(gp.xipol,gp.xipol,gp.parst.nu2)
gp.d2_x = phys.delta(gp.parst.delta2)
gp.sig2_x = phys.sigmaz(gp.xipol, gp.dens_x, gp.nu2_x, gp.parst.norm2,\
gp.parst.delta2, [Rsun,hr,hsig])
gp.kap2_x = gp.xipol*0.
if gp.checkint:
gp.nu1_x = gp.ipol.nudat1
Rsun = 8.; hr = 3.0; hsig = 3.0
# ^--- irrelevant, as long as gp.deltaprior = True
gp.parst.norm1 = 17.**2
gp.sig1_x = phys.sigmaz(gp.xipol, gp.ipol.densdat, gp.blow,\
gp.ipol.nudat1,gp.parst.norm1,gp.parst.delta1,\
[Rsun,hr,hsig])
gp.kap1_x = gp.xipol*0.
return
def calc_likelihood():
if gp.uselike:
# Errors:
if gp.adderrors:
# Solve error convolution: # can be jumped
prob_z = np.zeros(len(z_dat))
for jj in range(len(z_dat)):
zintmin = -abs(z_dat_err[jj])*3.0
zintmax = abs(z_dat_err[jj])*3.0
zintpnts = 25
zint = np.arange(zintpnts) * (zintmax-zintmin)/(zintpnts-1.)\
+ zintmin + z_dat[jj]
perr_z = errorz(z_dat[jj] - zint, z_dat_err[jj])
nu_int = nu(abs(zint),zp_kz,nupars)
p_z = nu_int
prob_z[jj] = simps(perr_z * p_z,zint)
if prob_z[jj] < 0:
print('Negative probability found!')
pdb.set_trace()
nu_pz = phys.nu(abs(z_dat),zp_kz,nupars)
prob_z = nu_pz
# FOR TESTING!
# test = gp.nu1_x
# gpl.plot(z_dat,test,psym=3)
# gpl.plot(z_dat,prob_z,color=2,psym=3)
# gpl.show(); exit(1)
sig_sum = sqrt(sig_z**2. + vz_dat_err**2.)
aprob_sigz = alog(1.0/(sqrt(2.0*np.pi) * sig_sum)) -\
(vz_dat-vz_mean)**2./2./sig_sum**2.
# *** WARNING *** TI< with errors not yet supported ... !
prob_tilt = 1.0
else:
# Calcualte likelihood [N.B. Log[Li] can be +ve!]:
prob_z = nu_z
aprob_sigz = np.log(1.0/(sqrt(2.0*np.pi) * sig_z))\
- (vz_dat-vz_mean)**2./2./sig_z**2.
if not gp.deltaprior:
wid_Rz = sigma_rz(abs(z_dat),zp_kz,tparswt)
prob_tilt = 1.0/np.pi/wid_Rz *\
BESELK(abs(vz_dat*vR_dat-sig_Rz)/wid_Rz,0)
else:
prob_tilt = 1.0
prob_t = np.sum(np.log(prob_z) + aprob_sigz + np.log(prob_tilt))
if math.isnan(prob_t):
print('Ooops prob_t is NaN ... ')
pdb.set_trace()
# Calculate the likelihood ratio:
fnewoverf = np.exp(prob_t - prob)
return gp.fnewoverf
def calc_M_nu_sig_kap_sphere():
import physics_sphere as phys
if not gp.checkint:
'''normal case'''
gp.nu1_x = phys.nu(gp.parst.nu1) # [munit/pc^3]
gp.dens_x = phys.dens(gp.xipol, gp.parst.dens) # [munit/pc^3]
gp.M_x = rho_SUM_Mr(gp.xipol, gp.dens_x) # [munit,3D]
gp.d1_x = phys.delta(gp.parst.delta1)
# pdb.set_trace()
# TODO: introduce error in dens, nu, d1 here, for weights in splrep
gp.sig1_x, gp.kap1_x = phys.sig_kap_los(gp.dens_x, gp.nu1_x, gp.d1_x)
if gp.pops == 2:
gp.nu2_x = phys.nu(gp.parst.nu2)
gp.d2_x = phys.delta(gp.parst.delta2)
gp.sig2_x, gp.kap2_x = phys.sig_kap_los(gp.dens_x, gp.nu2_x, gp.d2_x)
# to debug almost fitting curves:
# if gp.chi2 < 60:
# pdb.set_trace()
else:
'checkint: check integration routines for sigma, kappa'
if gp.investigate == 'hernquist':
### set nu to data, or analytic value
gp.nu1_x = gp.ipol.nudat1 # [Msun/pc^3]
if gp.analytic:
gp.nu1_x = rho_anf(gp.xipol) # [Msun/pc^3]
# set dens
gp.dens_x = rho_anf(gp.xipol) # [Msun/pc^3]
# attention: same as gp.nu1_x in this case!
# fine if potential comes from 'tracers' only
gp.M_x = M_anf(gp.xipol) # [Msun]
gp.d1_x = np.zeros(gp.nipol) # [1]
print('set nu, dens, M, delta to analytic')
gp.sig1_x, gp.kap1_x = phys.sig_kap_los(gp.dens_x, gp.nu1_x, gp.d1_x)
# still checkint, but for walker now
elif gp.investigate == 'walker':
rhodm, rhostar1, rhostar2 = rhowalker_3D(gp.xipol)
gp.nu1_x = rhostar1
if gp.pops == 2:
gp.nu2_x = rhostar2 # gp.ipol.nudat2 # data
gp.dens_x = rhowalkertot_3D(gp.xipol) # [msun/pc^3]
gp.M_x = Mwalkertot(gp.xipol) # should really be this thing here!
# gp.M_x = rho_SUM_Mr(gp.ipol.Mx, gp.dens_x) # [msun]
# gp.M_x = rho_SUM_Mr(gp.ipol.binmax, gp.dens_x) # [msun]
gp.d1_x = walker_delta(1) # [1], actual delta
if gp.pops == 2:
gp.d2_x = walker_delta(2) # [1]
# set sigma_LOS, by calculating expected values
# for both (spherical) cases simultaneously
gp.sig1_x,gp.kap1_x = phys.sig_kap_los(gp.dens_x, gp.nu1_x, \
gp.d1_x) # [km/s], [1]
# takes [munit, 3D], [munit/pc^3], [1]
# normalization of nu does not matter, is divided out
if gp.pops == 2:
gp.sig2_x, gp.kap2_x = phys.sig_kap_los(gp.dens_x,gp.nu2_x,\
gp.d2_x) # [km/s], [1]
return
def accept_reject(n):
if npr.rand() < gp.fnewoverf:
gp.accrate.update(True)
gp.pars.assign(gp.parst)
gp.chi2 = gp.chi2t
gp.lasterr = 'None'
gp.d1wild = False; gp.d2wild = False; gp.dens2wild = False
gp.b2wild = False; gp.sig2wild = False; gp.nu2wild = 1000
gfile.store_working_pars(n, gp.pars, gp.chi2, gp.parstep)
# fplot
if npr.rand() < max(0.01, (1.*gp.chi2-gp.chi2tol)/gp.chi2tol/100.)\
or (gp.initphase and gp.adaptstepwait == 1):
gpl.update_plot()
np.set_printoptions(precision=3)
if gp.pops == 1:
gp.LOG.warning('n: %d, chi2:'+gh.pretty(gp.chi2,1)+\
' rate:'+gh.pretty(100*gp.accrate.rate(),2)+\
' nu1:'+gh.pretty(100*\
abs(np.median((phys.nu(gp.pars.nu1+gp.parstep.nu1)\
-phys.nu(gp.pars.nu1))/\
phys.nu(gp.pars.nu1))),3)+\
' d1:'+gh.pretty(100*\
abs(np.median(gp.parstep.delta1/gp.pars.delta1)),3)+\
' dens:'+gh.pretty(100*\
abs(np.median((phys.densdefault(gp.parstep.dens+\
gp.pars.dens)\
-phys.densdefault(gp.pars.dens))/\
phys.densdefault(gp.pars.dens))),3)+\
' norm1:'+gh.pretty(100*\
abs(np.median(gp.parstep.norm1/gp.pars.norm1)),3), n)
else:
print('n:',n, ' chi2:',gh.pretty(gp.chi2,1),\
' rate:',gh.pretty(100*gp.accrate.rate(),2),\
' nu1:',gh.pretty(100*\
abs(np.median((phys.nu(gp.pars.nu1+gp.parstep.nu1)\
-phys.nu(gp.pars.nu1))/\
phys.nu(gp.pars.nu1))),3),\
' nu2:',gh.pretty(100*\
abs(np.median((phys.nu(gp.pars.nu2+gp.parstep.nu2)\
-phys.nu(gp.pars.nu2))/\
phys.nu(gp.pars.nu2))),3),\
' d1:',gh.pretty(100*\
abs(np.median(gp.parstep.delta1/gp.pars.delta1)),3),\
' d2:',gh.pretty(100*
abs(np.median(gp.parstep.delta2/gp.pars.delta2)),3),\
' dens:',gh.pretty(100*\
abs(np.median((phys.densdefault(gp.parstep.dens+\
gp.pars.dens)-phys.densdefault(gp.pars.dens))/\
phys.densdefault(gp.pars.dens))),3))
adapt_stepsize()
end_initphase()
else:
gp.accrate.update(False)
# jump back to last known good point
faraway = gp.farinit if gp.initphase else gp.farover
if gp.chi2t > gp.chi2 * faraway:
gp.LOG.warning(' too far off, setting back to last known good point')
gfile.get_working_pars(scale=False)
# TODO: check that scale=gp.iniphase is really not the right thing
return
