def test_king_sigmar_directint():
pot= potential.KingPotential(W0=3.,M=2.3,rt=1.76)
dfk= kingdf(W0=3.,M=2.3,rt=1.76)
tol= 0.05 # Jeans isn't that accurate for this rather difficult case
check_sigmar_against_jeans_directint(dfk,pot,tol,beta=0.,
rmin=dfk._scale/10.,
rmax=dfk.rt*0.7,bins=31)
return None
def test_king_dens_directint():
pot= potential.KingPotential(W0=3.,M=2.3,rt=1.76)
dfk= kingdf(W0=3.,M=2.3,rt=1.76)
tol= 0.02
check_dens_directint(dfk,pot,tol,
lambda r: dfk.dens(r)/2.3, # need to divide by mass
rmin=dfk._scale/10.,
rmax=dfk.rt*0.7,bins=31)
return None
def test_king_dens_massprofile():
pot= potential.KingPotential(W0=3.,M=2.3,rt=1.76)
dfk= kingdf(W0=3.,M=2.3,rt=1.76)
numpy.random.seed(10)
samp= dfk.sample(n=100000)
tol= 1e-2
check_spherical_massprofile(samp,lambda r: pot.mass(r)\
/pot.mass(numpy.amax(samp.r())),
tol,skip=4000)
return None
def test_king_beta():
pot= potential.KingPotential(W0=3.,M=2.3,rt=1.76)
dfk= kingdf(W0=3.,M=2.3,rt=1.76)
numpy.random.seed(10)
samp= dfk.sample(n=1000000)
tol= 6*1e-2
# lower tolerance closer to rt because fewer stars there
tol= 0.12
check_beta(samp,pot,tol,beta=0.,rmin=dfk._scale/10.,rmax=dfk.rt,
bins=31)
return None
def test_king_sigmar():
W0s= [1.,3.,9.]
for W0 in W0s:
pot= potential.KingPotential(W0=W0,M=2.3,rt=1.76)
dfk= kingdf(W0=W0,M=2.3,rt=1.76)
numpy.random.seed(10)
samp= dfk.sample(n=1000000)
# lower tolerance closer to rt because fewer stars there
tol= 0.09
check_sigmar_against_jeans(samp,pot,tol,beta=0.,
rmin=dfk._scale/10.,rmax=dfk.rt*0.7,bins=31)
tol= 0.2
check_sigmar_against_jeans(samp,pot,tol,beta=0.,
rmin=dfk.rt*0.8,rmax=dfk.rt*0.95,bins=5)
return None