def plot_dfcorrections(plotfilename):
niters= [1,2,3,4,5,10,15,20,25]
bovy_plot.bovy_print(fig_height=7.,fig_width=8.)
ii= 0
# Load DF
pyplot.subplot(2,1,1)
dfc= dehnendf(beta=0.,correct=True,niter=niters[ii])
bovy_plot.bovy_plot(dfc._corr._rs,
numpy.log(dfc._corr._corrections[:,0]),
'-',gcf=True,color=cm.jet(1.),lw=2.,zorder=1,
xrange=[0.,5.],
yrange=[-0.25,0.25],
ylabel=r'$\ln \Sigma_{\mathrm{out}}(R)-\ln\Sigma_{\mathrm{DF}}(R)$')
linthresh= 0.0001
pyplot.yscale('symlog',linthreshy=linthresh)
for ii,niter in enumerate(niters[1:]):
dfcn= dehnendf(beta=0.,correct=True,niter=niter)
dfcp= dehnendf(beta=0.,correct=True,niter=niter-1)
bovy_plot.bovy_plot(dfc._corr._rs,
numpy.log(dfcn._corr._corrections[:,0])-numpy.log(dfcp._corr._corrections[:,0]),
'-',overplot=True,
color=cm.jet(1.-(ii+1)/float(len(niters))),lw=2.,
zorder=ii+2)
pyplot.fill_between(numpy.linspace(0.,5.,2.),
-linthresh*numpy.ones(2),
linthresh*numpy.ones(2),color='0.9',
zorder=0)
bovy_plot.bovy_text(4.,-0.00008,r'$\mathrm{linear\ scale}$',
backgroundcolor='w',size=16.)
pyplot.subplot(2,1,2)
bovy_plot.bovy_plot(dfc._corr._rs,
0.5*numpy.log(dfc._corr._corrections[:,1]),
'-',gcf=True,color=cm.jet(1.),lw=2.,zorder=1,
xrange=[0.,5.],
yrange=[-0.25,0.25],
xlabel=r'$R/R_0$',
ylabel=r'$\ln \sigma_{R,\mathrm{out}}(R)-\ln\sigma_{R,\mathrm{DF}}(R)$')
pyplot.yscale('symlog',linthreshy=linthresh)
for ii,niter in enumerate(niters[1:]):
dfcn= dehnendf(beta=0.,correct=True,niter=niter)
dfcp= dehnendf(beta=0.,correct=True,niter=niter-1)
bovy_plot.bovy_plot(dfc._corr._rs,
numpy.log(dfcn._corr._corrections[:,1])-numpy.log(dfcp._corr._corrections[:,1]),
'-',overplot=True,
color=cm.jet(1.-(ii+1)/float(len(niters))),lw=2.,
zorder=ii+2)
pyplot.fill_between(numpy.linspace(0.,5.,2.),
-linthresh*numpy.ones(2),
linthresh*numpy.ones(2),color='0.9',
zorder=0)
bovy_plot.bovy_text(4.,-0.00008,r'$\mathrm{linear\ scale}$',
backgroundcolor='w',size=16.)
pyplot.tight_layout()
bovy_plot.bovy_end_print(plotfilename)
return None
def test_mildnonaxi_meanvr_grid():
# Test that for a close to axisymmetric potential, the mean vr is close to zero
idf= dehnendf(beta=0.)
pot= [LogarithmicHaloPotential(normalize=1.),
SteadyLogSpiralPotential(A=-0.005,omegas=0.2)] #very mild non-axi
edf= evolveddiskdf(idf,pot=pot,to=-10.)
mvr, grid= edf.meanvR(0.9,phi=0.2,integrate_method='rk6_c',grid=True,
returnGrid=True,gridpoints=_GRIDPOINTS)
assert numpy.fabs(mvr) < 0.001, 'meanvR of evolveddiskdf for axisymmetric potential is not equal to zero'
mvr= edf.meanvR(0.9,phi=0.2,integrate_method='rk6_c',grid=grid)
assert numpy.fabs(mvr) < 0.001, 'meanvR of evolveddiskdf for axisymmetric potential is not equal to zero when calculated with pre-computed grid'
#Pre-compute surfmass and use it, first test that grid is properly returned when given
smass, ngrid= edf.vmomentsurfacemass(0.9,0,0,phi=0.2,
integrate_method='rk6_c',
grid=grid,gridpoints=_GRIDPOINTS,
returnGrid=True)
assert ngrid == grid, 'grid returned by vmomentsurfacemass w/ grid input is not the same as the input'
#Pre-compute surfmass and use it
nsmass= edf.vmomentsurfacemass(0.9,0,0,phi=0.2,
integrate_method='rk6_c',
grid=True,gridpoints=_GRIDPOINTS)
assert numpy.fabs(smass-nsmass) < 0.001, 'surfacemass computed w/ and w/o returnGrid are not the same'
mvr= edf.meanvR(0.9,phi=0.2,integrate_method='rk6_c',grid=grid,
surfacemass=smass)
assert numpy.fabs(mvr) < 0.001, 'meanvR of evolveddiskdf for axisymmetric potential is not equal to zero when calculated with pre-computed grid and surfacemass'
global _maxi_meanvr
_maxi_meanvr= mvr
global _maxi_surfacemass
_maxi_surfacemass= smass
return None
def test_elliptical_cold_vertexdev():
# Test that the vertex deviations for the elliptical disk behaves as analytically expected
idf = dehnendf(beta=0., profileParams=(1. / 3., 1., 0.0125))
cp = 0.05
pot = [
LogarithmicHaloPotential(normalize=1.),
EllipticalDiskPotential(cp=cp, sp=0., p=0., tform=-150., tsteady=125.)
]
edf = evolveddiskdf(idf, pot=pot, to=-150.)
#Should be -2cp in radians
vdev, grid = edf.vertexdev(0.9,
phi=-numpy.pi / 4.,
integrate_method='rk6_c',
grid=True,
nsigma=7.,
returnGrid=True,
gridpoints=_GRIDPOINTS)
assert numpy.fabs(
vdev + 2. * cp
) < 10.**-3., 'Cold elliptical disk does not agree with analytical calculation for vertexdev'
#Should be 0
vdev, grid = edf.vertexdev(0.9,
phi=0.,
integrate_method='rk6_c',
grid=True,
nsigma=7.,
returnGrid=True,
gridpoints=_GRIDPOINTS)
assert numpy.fabs(
vdev
) < 10.**-2. / 180. * numpy.pi, 'Cold elliptical disk does not agree with analytical calculation for vertexdev'
return None
def test_call_marginalizevlos():
from galpy.orbit import Orbit
idf = dehnendf(beta=0.)
pot = [
LogarithmicHaloPotential(normalize=1.),
EllipticalDiskPotential(twophio=0.001)
] #very mild non-axi
edf = evolveddiskdf(idf, pot=pot[0], to=-10.) #one with just one potential
#l=0
R, phi, vT = 0.8, 0., 0.7
vrs = numpy.linspace(-1., 1., 101)
pvrs = numpy.array(
[edf(Orbit([R, vr, vT, phi]), integrate_method='rk6_c') for vr in vrs])
assert numpy.fabs(numpy.log(numpy.sum(pvrs)*(vrs[1]-vrs[0]))\
-edf(Orbit([R,0.,vT,phi]),marginalizeVlos=True,integrate_method='rk6_c',log=True)) < 10.**-4., 'diskdf call w/ marginalizeVlos does not work'
#l=270, this DF has some issues, but it suffices to test the mechanics of the code
edf = evolveddiskdf(idf, pot=pot, to=-10.)
R, phi, vR = numpy.sin(numpy.pi / 6.), -numpy.pi / 3., 0.4 #l=30 degree
vts = numpy.linspace(0.3, 1.5, 101)
pvts = numpy.array(
[edf(Orbit([R, vR, vt, phi]), integrate_method='rk6_c') for vt in vts])
assert numpy.fabs(numpy.sum(pvts)*(vts[1]-vts[0])\
-edf(Orbit([R,vR,0.,phi]),
marginalizeVlos=True,
integrate_method='rk6_c',
nsigma=4)) < 10.**-3.5, 'diskdf call w/ marginalizeVlos does not work'
return None
def test_mildnonaxi_meanvt_hierarchgrid_tlist():
# Test that for a close to axisymmetric potential, the mean vt is close to that of the initial DF
idf = dehnendf(beta=0.)
pot = [
LogarithmicHaloPotential(normalize=1.),
SteadyLogSpiralPotential(A=-0.005, omegas=0.2)
] #very mild non-axi
edf = evolveddiskdf(idf, pot=pot, to=-10.)
mvt, grid = edf.meanvT(0.9,
t=[0., -2.5, -5., -7.5, -10.],
phi=0.2,
integrate_method='rk6_c',
grid=True,
hierarchgrid=True,
returnGrid=True,
gridpoints=_GRIDPOINTS)
assert numpy.all(
numpy.fabs(mvt - idf.meanvT(0.9)) < 0.005
), 'meanvT of evolveddiskdf for axisymmetric potential is not equal to that of the initial dehnendf when using hierarchgrid and tlist'
mvt = edf.meanvT(0.9,
t=[0., -2.5, -5., -7.5, -10.],
phi=0.2,
integrate_method='rk6_c',
grid=grid,
gridpoints=_GRIDPOINTS)
assert numpy.all(
numpy.fabs(mvt - idf.meanvT(0.9)) < 0.005
), 'meanvT of evolveddiskdf for axisymmetric potential is not equal to that of the initial dehnendf when calculated with pre-computed grid when using hierarchgrid and tlist'
return None
def test_mildnonaxi_meanvt_hierarchgrid():
# Test that for a close to axisymmetric potential, the mean vt is close to that of the initial DF
idf= dehnendf(beta=0.)
pot= [LogarithmicHaloPotential(normalize=1.),
SteadyLogSpiralPotential(A=-0.005,omegas=0.2)] #very mild non-axi
edf= evolveddiskdf(idf,pot=pot,to=-10.)
mvt, grid= edf.meanvT(0.9,phi=0.2,integrate_method='rk6_c',
grid=True,hierarchgrid=True,
returnGrid=True,gridpoints=_GRIDPOINTS)
assert numpy.fabs(mvt-idf.meanvT(0.9)) < 0.005, 'meanvT of evolveddiskdf for axisymmetric potential is not equal to that of the initial dehnendf when using hierarchgrid'
mvt= edf.meanvT(0.9,phi=0.2,integrate_method='rk6_c',grid=grid,
gridpoints=_GRIDPOINTS)
assert numpy.fabs(mvt-idf.meanvT(0.9)) < 0.005, 'meanvT of evolveddiskdf for axisymmetric potential is not equal to that of the initial dehnendf when calculated with pre-computed grid when using hierarchgrid'
#Also test that the hierarchgrid is properly returned
smass, ngrid= edf.vmomentsurfacemass(0.9,0,0,phi=0.2,
integrate_method='rk6_c',
grid=grid,gridpoints=_GRIDPOINTS,
returnGrid=True)
assert ngrid == grid, 'hierarchical grid returned by vmomentsurfacemass w/ grid input is not the same as the input'
nsmass= edf.vmomentsurfacemass(0.9,0,0,phi=0.2,
integrate_method='rk6_c',
grid=True,hierarchgrid=True,
gridpoints=_GRIDPOINTS)
assert numpy.fabs(smass-nsmass) < 0.001, 'surfacemass computed w/ and w/o returnGrid are not the same'
return None
def test_call_marginalizevperp():
from galpy.orbit import Orbit
idf = dehnendf(beta=0.)
pot = [
LogarithmicHaloPotential(normalize=1.),
EllipticalDiskPotential(twophio=0.001)
] #very mild non-axi
edf = evolveddiskdf(idf, pot=pot[0], to=-10.) #one with just one potential
#l=0
R, phi, vR = 0.8, 0., 0.4
vts = numpy.linspace(0., 1.5, 51)
pvts = numpy.array(
[edf(Orbit([R, vR, vt, phi]), integrate_method='rk6_c') for vt in vts])
assert numpy.fabs(numpy.sum(pvts)*(vts[1]-vts[0])\
-edf(Orbit([R,vR,0.,phi]),marginalizeVperp=True,integrate_method='rk6_c')) < 10.**-3.5, 'evolveddiskdf call w/ marginalizeVperp does not work'
#l=270
edf = evolveddiskdf(idf, pot=pot, to=-10.)
R, phi, vT = numpy.sin(numpy.pi / 6.), -numpy.pi / 3., 0.7 #l=30 degree
vrs = numpy.linspace(-1., 1., 101)
pvrs = numpy.array(
[edf(Orbit([R, vr, vT, phi]), integrate_method='rk6_c') for vr in vrs])
assert numpy.fabs(numpy.log(numpy.sum(pvrs)*(vrs[1]-vrs[0]))\
-edf(Orbit([R,0.,vT,phi]),
marginalizeVperp=True,
integrate_method='rk6_c',log=True,
nsigma=4)) < 10.**-2.5, 'evolveddiskdf call w/ marginalizeVperp does not work'
return None
def test_mildnonaxi_meanvr_grid_tlist():
# Test that for a close to axisymmetric potential, the mean vr is close to zero
idf = dehnendf(beta=0.)
pot = [
LogarithmicHaloPotential(normalize=1.),
SteadyLogSpiralPotential(A=-0.005, omegas=0.2)
] #very mild non-axi
edf = evolveddiskdf(idf, pot=pot, to=-10.)
mvr, grid = edf.meanvR(0.9,
t=[0., -2.5, -5., -7.5, -10.],
phi=0.2,
integrate_method='rk6_c',
grid=True,
returnGrid=True,
gridpoints=_GRIDPOINTS)
assert numpy.all(
numpy.fabs(mvr) < 0.003
), 'meanvR of evolveddiskdf for axisymmetric potential is not equal to zero for list of times'
mvr = edf.meanvR(0.9,
t=[0., -2.5, -5., -7.5, -10.],
phi=0.2,
integrate_method='rk6_c',
grid=grid)
assert numpy.all(
numpy.fabs(mvr) < 0.003
), 'meanvR of evolveddiskdf for axisymmetric potential is not equal to zero when calculated with pre-computed grid for list of times'
return None
def test_elliptical_cold_vt():
# Test that the rotational velocity for the elliptical disk behaves as analytically expected
idf = dehnendf(beta=0., profileParams=(1. / 3., 1., 0.0125))
cp = 0.05
pot = [
LogarithmicHaloPotential(normalize=1.),
EllipticalDiskPotential(cp=cp, sp=0., p=0., tform=-150., tsteady=125.)
]
edf = evolveddiskdf(idf, pot=pot, to=-150.)
#Should be 1.
mvt, grid = edf.meanvT(0.9,
phi=-numpy.pi / 4.,
integrate_method='rk6_c',
grid=True,
returnGrid=True,
gridpoints=_GRIDPOINTS)
assert numpy.fabs(
mvt - 1.
) < 10.**-3., 'Cold elliptical disk does not agree with analytical calculation for vt'
#Should be 1.-cp
mvt, grid = edf.meanvT(0.9,
phi=0.,
integrate_method='rk6_c',
grid=True,
nsigma=7.,
returnGrid=True,
gridpoints=_GRIDPOINTS)
assert numpy.fabs(
mvt - 1. + cp
) < 10.**-3., 'Cold elliptical disk does not agree with analytical calculation for vt'
return None
def test_mildnonaxi_oortK_grid():
# Test that for a close to axisymmetric potential, the oortK is close to zero
idf = dehnendf(beta=0.)
pot = [
LogarithmicHaloPotential(normalize=1.),
EllipticalDiskPotential(twophio=0.001)
] #very mild non-axi
edf = evolveddiskdf(idf, pot=pot, to=-10.)
ok, grid, dgridR, dgridphi=\
edf.oortK(0.9,phi=0.2,integrate_method='rk6_c',
grid=True,derivRGrid=True,derivphiGrid=True,
returnGrids=True,
gridpoints=_GRIDPOINTS,derivGridpoints=_GRIDPOINTS)
assert numpy.fabs(
ok
) < 0.005, 'oortK of evolveddiskdf for axisymmetric potential is not equal to that of initial DF'
ok = edf.oortK(0.9,
phi=0.2,
integrate_method='rk6_c',
grid=grid,
derivRGrid=dgridR,
derivphiGrid=dgridphi,
gridpoints=_GRIDPOINTS,
derivGridpoints=_GRIDPOINTS)
assert numpy.fabs(
ok
) < 0.005, 'oortK of evolveddiskdf for axisymmetric potential is not equal to that of initial DF when calculated with pre-computed grid'
return None
def test_elliptical_cold_oortABCK_position2():
# Test that the Oort functions A, B, C, and K for the elliptical disk behaves as analytically expected
idf= dehnendf(beta=0.,profileParams=(1./3.,1.,0.0125))
cp= 0.05
pot= [LogarithmicHaloPotential(normalize=1.),
EllipticalDiskPotential(cp=cp,sp=0.,p=0.,tform=-150.,tsteady=125.)]
edf= evolveddiskdf(idf,pot=pot,to=-150.)
#Should be 0.5/0.9+cp/2
oorta, grid, gridr, gridp= edf.oortA(0.9,phi=0.,
integrate_method='rk6_c',grid=True,
nsigma=7.,
derivRGrid=True,derivphiGrid=True,
returnGrids=True,
gridpoints=51,
derivGridpoints=51)
assert numpy.fabs(oorta-cp/2.-0.5/0.9) < 10.**-2.2, 'Cold elliptical disk does not agree with analytical calculation for oortA'
#Should be -cp/2-0.5/0.9
oortb= edf.oortB(0.9,phi=0.,
integrate_method='rk6_c',grid=grid,nsigma=7.,
derivRGrid=gridr,derivphiGrid=gridp)
assert numpy.fabs(oortb+cp/2.+0.5/0.9) < 10.**-2.2, 'Cold elliptical disk does not agree with analytical calculation for oortB'
#Should be 0
oortc= edf.oortC(0.9,phi=0.,
integrate_method='rk6_c',grid=grid,nsigma=7.,
derivRGrid=gridr,derivphiGrid=gridp)
assert numpy.fabs(oortc) < 10.**-3., 'Cold elliptical disk does not agree with analytical calculation for oortC'
#Should be 0
oortk= edf.oortK(0.9,phi=0.,
integrate_method='rk6_c',grid=grid,nsigma=7.,
derivRGrid=gridr,derivphiGrid=gridp)
assert numpy.fabs(oortk) < 10.**-3., 'Cold elliptical disk does not agree with analytical calculation for oortK'
return None
def test_mildnonaxi_meanvr_grid():
# Test that for a close to axisymmetric potential, the mean vr is close to zero
idf= dehnendf(beta=0.)
pot= [LogarithmicHaloPotential(normalize=1.),
SteadyLogSpiralPotential(A=-0.005,omegas=0.2)] #very mild non-axi
edf= evolveddiskdf(idf,pot=pot,to=-10.)
mvr, grid= edf.meanvR(0.9,phi=0.2,integrate_method='rk6_c',grid=True,
returnGrid=True,gridpoints=_GRIDPOINTS)
assert numpy.fabs(mvr) < 0.001, 'meanvR of evolveddiskdf for axisymmetric potential is not equal to zero'
mvr= edf.meanvR(0.9,phi=0.2,integrate_method='rk6_c',grid=grid)
assert numpy.fabs(mvr) < 0.001, 'meanvR of evolveddiskdf for axisymmetric potential is not equal to zero when calculated with pre-computed grid'
#Pre-compute surfmass and use it, first test that grid is properly returned when given
smass, ngrid= edf.vmomentsurfacemass(0.9,0,0,phi=0.2,
integrate_method='rk6_c',
grid=grid,gridpoints=_GRIDPOINTS,
returnGrid=True)
assert ngrid == grid, 'grid returned by vmomentsurfacemass w/ grid input is not the same as the input'
#Pre-compute surfmass and use it
nsmass= edf.vmomentsurfacemass(0.9,0,0,phi=0.2,
integrate_method='rk6_c',
grid=True,gridpoints=_GRIDPOINTS)
assert numpy.fabs(smass-nsmass) < 0.001, 'surfacemass computed w/ and w/o returnGrid are not the same'
mvr= edf.meanvR(0.9,phi=0.2,integrate_method='rk6_c',grid=grid,
surfacemass=smass)
assert numpy.fabs(mvr) < 0.001, 'meanvR of evolveddiskdf for axisymmetric potential is not equal to zero when calculated with pre-computed grid and surfacemass'
global _maxi_meanvr
_maxi_meanvr= mvr
global _maxi_surfacemass
_maxi_surfacemass= smass
return None
def test_plot_hierarchgrid():
idf = dehnendf(beta=0.)
pot = [
LogarithmicHaloPotential(normalize=1.),
SteadyLogSpiralPotential(A=-0.005, omegas=0.2)
] #very mild non-axi
edf = evolveddiskdf(idf, pot=pot, to=-10.)
mvr, grid = edf.meanvR(0.9,
phi=0.2,
integrate_method='rk6_c',
grid=True,
hierarchgrid=True,
returnGrid=True,
gridpoints=_GRIDPOINTS)
grid.plot()
#w/ list of tiems
mvr, grid = edf.meanvR(0.9,
t=[0., -2.5, -5., -7.5, -10.],
phi=0.2,
integrate_method='rk6_c',
grid=True,
hierarchgrid=True,
returnGrid=True,
gridpoints=_GRIDPOINTS)
grid.plot(1)
return None
def create_fake_los(parser):
"""
NAME:
create_fake_los
PURPOSE:
create a fake APOGEE LOS
INPUT:
parser - from optparse
OUTPUT:
stuff as specified by the options
HISTORY:
2011-04-20 - Written - Bovy (NYU)
"""
(options, args) = parser.parse_args()
if len(args) == 0:
parser.print_help()
sys.exit(-1)
nu.random.seed(seed=options.seed)
#Set up DF
print "Setting up DF ..."
dfc = dehnendf(beta=options.beta,
profileParams=(options.rd, options.rs, options.so),
correct=True,
niter=20)
#Sample until we have nobjects
if options.local:
sample_local(options, args, dfc)
else:
sample_los(options, args, dfc)
return None
def test_mildnonaxi_meanvt_grid_tlist_onet():
# Test that for a close to axisymmetric potential, the mean vt is close to that of the initial DF, for a list consisting of a single time
idf = dehnendf(beta=0.)
pot = [
LogarithmicHaloPotential(normalize=1.),
SteadyLogSpiralPotential(A=-0.005, omegas=0.2)
] #very mild non-axi
edf = evolveddiskdf(idf, pot=pot, to=-10.)
mvt, grid = edf.meanvT(0.9,
t=[0.],
phi=0.2,
integrate_method='rk6_c',
grid=True,
returnGrid=True,
gridpoints=_GRIDPOINTS)
assert numpy.fabs(
mvt - idf.meanvT(0.9)
) < 0.005, 'meanvT of evolveddiskdf for axisymmetric potential is not equal to that of the initial dehnendf'
mvt = edf.meanvT(
0.9,
t=[0.],
phi=0.2,
integrate_method='rk6_c',
grid=grid,
gridpoints=_GRIDPOINTS,
)
assert numpy.fabs(
mvt - idf.meanvT(0.9)
) < 0.005, 'meanvT of evolveddiskdf for axisymmetric potential is not equal to that of the initial dehnendf when calculated with pre-computed grid'
global _maxi_meanvt
_maxi_meanvt = mvt
return None
def create_fake_los(parser):
"""
NAME:
create_fake_los
PURPOSE:
create a fake APOGEE LOS
INPUT:
parser - from optparse
OUTPUT:
stuff as specified by the options
HISTORY:
2011-04-20 - Written - Bovy (NYU)
"""
(options,args)= parser.parse_args()
if len(args) == 0:
parser.print_help()
sys.exit(-1)
nu.random.seed(seed=options.seed)
#Set up DF
print "Setting up DF ..."
dfc= dehnendf(beta=options.beta,profileParams=(options.rd,options.rs,options.so),correct=True,niter=20)
#Sample until we have nobjects
if options.local:
sample_local(options,args,dfc)
else:
sample_los(options,args,dfc)
return None
def test_mildnonaxi_meanvt_hierarchgrid():
# Test that for a close to axisymmetric potential, the mean vt is close to that of the initial DF
idf= dehnendf(beta=0.)
pot= [LogarithmicHaloPotential(normalize=1.),
SteadyLogSpiralPotential(A=-0.005,omegas=0.2)] #very mild non-axi
edf= evolveddiskdf(idf,pot=pot,to=-10.)
mvt, grid= edf.meanvT(0.9,phi=0.2,integrate_method='rk6_c',
grid=True,hierarchgrid=True,
returnGrid=True,gridpoints=_GRIDPOINTS)
assert numpy.fabs(mvt-idf.meanvT(0.9)) < 0.005, 'meanvT of evolveddiskdf for axisymmetric potential is not equal to that of the initial dehnendf when using hierarchgrid'
mvt= edf.meanvT(0.9,phi=0.2,integrate_method='rk6_c',grid=grid,
gridpoints=_GRIDPOINTS)
assert numpy.fabs(mvt-idf.meanvT(0.9)) < 0.005, 'meanvT of evolveddiskdf for axisymmetric potential is not equal to that of the initial dehnendf when calculated with pre-computed grid when using hierarchgrid'
#Also test that the hierarchgrid is properly returned
smass, ngrid= edf.vmomentsurfacemass(0.9,0,0,phi=0.2,
integrate_method='rk6_c',
grid=grid,gridpoints=_GRIDPOINTS,
returnGrid=True)
assert ngrid == grid, 'hierarchical grid returned by vmomentsurfacemass w/ grid input is not the same as the input'
nsmass= edf.vmomentsurfacemass(0.9,0,0,phi=0.2,
integrate_method='rk6_c',
grid=True,hierarchgrid=True,
gridpoints=_GRIDPOINTS)
assert numpy.fabs(smass-nsmass) < 0.001, 'surfacemass computed w/ and w/o returnGrid are not the same'
return None
def test_mildnonaxi_oortA_grid_tlist():
# Test that for a close to axisymmetric potential, the oortA is close to the value of the initial DF
idf = dehnendf(beta=0.)
pot = [
LogarithmicHaloPotential(normalize=1.),
EllipticalDiskPotential(twophio=0.001)
] #very mild non-axi
edf = evolveddiskdf(idf, pot=pot, to=-10.)
oa, grid, dgridR, dgridphi=\
edf.oortA(0.9,t=[0.,-2.5,-5.,-7.5,-10.],
phi=0.2,integrate_method='rk6_c',
grid=True,derivRGrid=True,derivphiGrid=True,
returnGrids=True,
gridpoints=_GRIDPOINTS,derivGridpoints=_GRIDPOINTS)
ioa = idf.oortA(0.9)
assert numpy.all(
numpy.fabs(oa - ioa) < 0.005
), 'oortA of evolveddiskdf for axisymmetric potential is not equal to that of initial DF'
oa = edf.oortA(0.9,
t=[0., -2.5, -5., -7.5, -10.],
phi=0.2,
integrate_method='rk6_c',
grid=grid,
derivRGrid=dgridR,
derivphiGrid=dgridphi,
gridpoints=_GRIDPOINTS,
derivGridpoints=_GRIDPOINTS)
assert numpy.all(
numpy.fabs(oa - ioa) < 0.005
), 'oortA of evolveddiskdf for axisymmetric potential is not equal to that of initial DF when calculated with pre-computed grid'
return None
def test_elliptical_cold_oortABCK_position2():
# Test that the Oort functions A, B, C, and K for the elliptical disk behaves as analytically expected
idf= dehnendf(beta=0.,profileParams=(1./3.,1.,0.0125))
cp= 0.05
pot= [LogarithmicHaloPotential(normalize=1.),
EllipticalDiskPotential(cp=cp,sp=0.,p=0.,tform=-150.,tsteady=125.)]
edf= evolveddiskdf(idf,pot=pot,to=-150.)
#Should be 0.5/0.9+cp/2
oorta, grid, gridr, gridp= edf.oortA(0.9,phi=0.,
integrate_method='rk6_c',grid=True,
nsigma=7.,
derivRGrid=True,derivphiGrid=True,
returnGrids=True,
gridpoints=51,
derivGridpoints=51)
assert numpy.fabs(oorta-cp/2.-0.5/0.9) < 10.**-2.2, 'Cold elliptical disk does not agree with analytical calculation for oortA'
#Should be -cp/2-0.5/0.9
oortb= edf.oortB(0.9,phi=0.,
integrate_method='rk6_c',grid=grid,nsigma=7.,
derivRGrid=gridr,derivphiGrid=gridp)
assert numpy.fabs(oortb+cp/2.+0.5/0.9) < 10.**-2.2, 'Cold elliptical disk does not agree with analytical calculation for oortB'
#Should be 0
oortc= edf.oortC(0.9,phi=0.,
integrate_method='rk6_c',grid=grid,nsigma=7.,
derivRGrid=gridr,derivphiGrid=gridp)
assert numpy.fabs(oortc) < 10.**-3., 'Cold elliptical disk does not agree with analytical calculation for oortC'
#Should be 0
oortk= edf.oortK(0.9,phi=0.,
integrate_method='rk6_c',grid=grid,nsigma=7.,
derivRGrid=gridr,derivphiGrid=gridp)
assert numpy.fabs(oortk) < 10.**-3., 'Cold elliptical disk does not agree with analytical calculation for oortK'
return None
def test_oort():
from galpy.df import dehnendf
df= dehnendf(beta=0.,correct=True,niter=20,
profileParams=(1./3.,1.,0.1))
va= 1.-df.meanvT(1.) # asymmetric drift
A= df.oortA(1.)
B= df.oortB(1.)
return None
def test_oort():
from galpy.df import dehnendf
df= dehnendf(beta=0.,correct=True,niter=20,
profileParams=(1./3.,1.,0.1))
va= 1.-df.meanvT(1.) # asymmetric drift
A= df.oortA(1.)
B= df.oortB(1.)
return None
def _calc_pred(vs, location, l, dmax):
"""Calculate the predicted distribution"""
thisvs = vs
if _PREDICTCACHE:
savefilename = os.path.join(
os.getenv('DATADIR'), 'bovy', 'vclos',
'predictedVclosDistribution_%i_%.0f_%i_%.0f_%.2f_%i_%.2f.sav' %
(location, l, len(vs), _PREDICTVC, _PREDICTSIGMA, _PREDICTNDS,
dmax))
if _PREDICTCACHE and os.path.exists(savefilename):
#Load prediction
savefile = open(savefilename, 'rb')
pred_dist = pickle.load(savefile)
savefile.close()
else:
ds = numpy.linspace(0., dmax, _PREDICTNDS)
dfc = dehnendf(beta=0.,
profileParams=(1. / 3., 1., _PREDICTSIGMA),
correct=True,
niter=20)
thisl = l * _DEGTORAD
thisvsolar = numpy.dot(
_PREDICTVSOLAR, numpy.array([-numpy.cos(thisl),
numpy.sin(thisl)]))
thisvs += thisvsolar
thisvs /= _PREDICTVC
ii = 0
pred_dist = numpy.zeros(len(vs))
while ii < len(vs):
out = 0.
norm = 0.
for jj in range(_PREDICTNDS):
d = ds[jj]
R = math.sqrt(1. + d**2. - 2. * d * math.cos(thisl))
if R == 0.:
R += 0.0001
d += 0.0001
if 1. / math.cos(thisl) < d and math.cos(thisl) > 0.:
theta = math.pi - math.asin(d / R * math.sin(thisl))
else:
theta = math.asin(d / R * math.sin(thisl))
vR = -thisvs[ii] * math.cos(
theta + thisl) #vperp=0 for marginalization
vT = thisvs[ii] * math.sin(theta + thisl)
o = Orbit([R, vR, vT, theta])
surfmass = dfc.surfacemassLOS(d, thisl, deg=False)
out += surfmass * dfc(
o, marginalizeVperp=True) / dfc.targetSurfacemass(R)
norm += surfmass
pred_dist[ii] = out / norm
ii += 1
if _PREDICTCACHE:
savefile = open(savefilename, 'wb')
try:
pickle.dump(pred_dist, savefile)
finally:
savefile.close()
return pred_dist
def test_mildnonaxi_meanvt_direct():
# Test that for a close to axisymmetric potential, the mean vt is close to that of the initial DF
# We do this for an axisymmetric potential, bc otherwise it takes too long
idf= dehnendf(beta=0.)
pot= [LogarithmicHaloPotential(normalize=1.)]
edf= evolveddiskdf(idf,pot=pot,to=-10.)
mvt= edf.meanvT(0.9,phi=0.2,integrate_method='rk6_c',grid=False)
assert numpy.fabs(mvt-idf.meanvT(0.9)) < 0.001, 'meanvT of evolveddiskdf for axisymmetric potential is not equal to that of the initial dehnendf when using direct integration'
return None
def test_mildnonaxi_meanvt_direct():
# Test that for a close to axisymmetric potential, the mean vt is close to that of the initial DF
# We do this for an axisymmetric potential, bc otherwise it takes too long
idf= dehnendf(beta=0.)
pot= [LogarithmicHaloPotential(normalize=1.)]
edf= evolveddiskdf(idf,pot=pot,to=-10.)
mvt= edf.meanvT(0.9,phi=0.2,integrate_method='rk6_c',grid=False)
assert numpy.fabs(mvt-idf.meanvT(0.9)) < 0.001, 'meanvT of evolveddiskdf for axisymmetric potential is not equal to that of the initial dehnendf when using direct integration'
return None
def test_mildnonaxi_meanvr_direct():
# Test that for an axisymmetric potential, the mean vr is close to zero
# We do this for an axisymmetric potential, bc otherwise it takes too long
idf= dehnendf(beta=0.)
pot= [LogarithmicHaloPotential(normalize=1.)]
edf= evolveddiskdf(idf,pot=pot,to=-10.)
mvr= edf.meanvR(0.9,phi=0.2,integrate_method='rk6_c',grid=False)
assert numpy.fabs(mvr) < 0.001, 'meanvR of evolveddiskdf for axisymmetric potential is not equal to zero when calculated directly'
return None
def test_mildnonaxi_vertexdev_direct():
# Test that for an axisymmetric potential, the vertex deviation is close zero
# We do this for an axisymmetric potential, bc otherwise it takes too long
idf= dehnendf(beta=0.)
pot= [LogarithmicHaloPotential(normalize=1.)]
edf= evolveddiskdf(idf,pot=pot,to=-10.)
vdev= edf.vertexdev(0.9,phi=0.2,integrate_method='rk6_c',grid=False)
assert numpy.fabs(vdev) < 0.01/180.*numpy.pi, 'vertexdev of evolveddiskdf for axisymmetric potential is not equal to zero when calculated directly'
return None
def test_mildnonaxi_meanvr_direct():
# Test that for an axisymmetric potential, the mean vr is close to zero
# We do this for an axisymmetric potential, bc otherwise it takes too long
idf= dehnendf(beta=0.)
pot= [LogarithmicHaloPotential(normalize=1.)]
edf= evolveddiskdf(idf,pot=pot,to=-10.)
mvr= edf.meanvR(0.9,phi=0.2,integrate_method='rk6_c',grid=False)
assert numpy.fabs(mvr) < 0.001, 'meanvR of evolveddiskdf for axisymmetric potential is not equal to zero when calculated directly'
return None
def test_mildnonaxi_sigmat2_direct():
# Test that for an axisymmetric potential, the sigmaT2 is close to the value of the initial DF
# We do this for an axisymmetric potential, bc otherwise it takes too long
idf= dehnendf(beta=0.)
pot= [LogarithmicHaloPotential(normalize=1.)]
edf= evolveddiskdf(idf,pot=pot,to=-10.)
st2= edf.sigmaT2(0.9,phi=0.2,integrate_method='rk6_c',grid=False)
ist2= idf.sigmaT2(0.9)
assert numpy.fabs(numpy.log(st2)-numpy.log(ist2)) < 0.025, 'sigmat2 of evolveddiskdf for axisymmetric potential is not equal to that of initial DF when calculated directly'
return None
def test_mildnonaxi_sigmat2_direct():
# Test that for an axisymmetric potential, the sigmaT2 is close to the value of the initial DF
# We do this for an axisymmetric potential, bc otherwise it takes too long
idf= dehnendf(beta=0.)
pot= [LogarithmicHaloPotential(normalize=1.)]
edf= evolveddiskdf(idf,pot=pot,to=-10.)
st2= edf.sigmaT2(0.9,phi=0.2,integrate_method='rk6_c',grid=False)
ist2= idf.sigmaT2(0.9)
assert numpy.fabs(numpy.log(st2)-numpy.log(ist2)) < 0.025, 'sigmat2 of evolveddiskdf for axisymmetric potential is not equal to that of initial DF when calculated directly'
return None
def test_mildnonaxi_meanvt_direct_tlist():
# Shouldn't work
idf= dehnendf(beta=0.)
pot= [LogarithmicHaloPotential(normalize=1.)]
edf= evolveddiskdf(idf,pot=pot,to=-10.)
try:
edf.meanvT(0.9,t=[0.,-2.5,-5.,-7.5,-10.],
phi=0.2,integrate_method='rk6_c',grid=False)
except IOError: pass
else: raise AssertionError('direct evolveddiskdf calculation of meanvT w/ list of times did not raise IOError')
return None
def test_mildnonaxi_vertexdev_direct():
# Test that for an axisymmetric potential, the vertex deviation is close zero
# We do this for an axisymmetric potential, bc otherwise it takes too long
idf = dehnendf(beta=0.)
pot = [LogarithmicHaloPotential(normalize=1.)]
edf = evolveddiskdf(idf, pot=pot, to=-10.)
vdev = edf.vertexdev(0.9, phi=0.2, integrate_method='rk6_c', grid=False)
assert numpy.fabs(
vdev
) < 0.01 / 180. * numpy.pi, 'vertexdev of evolveddiskdf for axisymmetric potential is not equal to zero when calculated directly'
return None
def test_mildnonaxi_meanvt_direct_tlist():
# Shouldn't work
idf= dehnendf(beta=0.)
pot= [LogarithmicHaloPotential(normalize=1.)]
edf= evolveddiskdf(idf,pot=pot,to=-10.)
try:
edf.meanvT(0.9,t=[0.,-2.5,-5.,-7.5,-10.],
phi=0.2,integrate_method='rk6_c',grid=False)
except IOError: pass
else: raise AssertionError('direct evolveddiskdf calculation of meanvT w/ list of times did not raise IOError')
return None
def test_meanlz():
#This is a *very* rough test against a rough estimate of the mean
qdf= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
pot=MWPotential,aA=aAS,cutcounter=True)
from galpy.df import dehnendf #baseline
dfc= dehnendf(profileParams=(1./4.,1.0, 0.2),
beta=0.,correct=False)
assert numpy.fabs(numpy.log(qdf.meanlz(0.9,0.,mc=True))\
-numpy.log(0.9*dfc.meanvT(0.9))) < 0.1, 'meanlz is not what is expected'
assert numpy.fabs(numpy.log(qdf.meanlz(0.5,0.,mc=True))\
-numpy.log(0.5*dfc.meanvT(0.5))) < 0.2, 'meanlz is not what is expected'
return None
def test_call_special():
from galpy.orbit import Orbit
idf= dehnendf(beta=0.)
pot= [LogarithmicHaloPotential(normalize=1.),
EllipticalDiskPotential(twophio=0.001)] #very mild non-axi
edf= evolveddiskdf(idf,pot=pot,to=-10.)
o= Orbit([0.9,0.1,1.1,2.])
#call w/ and w/o explicit t
assert numpy.fabs(numpy.log(edf(o,0.))-numpy.log(edf(o))) < 10.**-10., 'edf.__call__ w/ explicit t=0. and w/o t do not give the same answer'
#call must get Orbit, otherwise error
try: edf(0.9,0.1,1.1,2.)
except IOError: pass
else: raise AssertionError('edf.__call__ w/o Orbit input did not raise IOError')
#Call w/ list, but just to
assert numpy.fabs(numpy.log(edf(o,[-10.]))-numpy.log(idf(o))) < 10.**-10., 'edf.__call__ w/ tlist set to [to] did not return initial DF'
#Call w/ just to
assert numpy.fabs(numpy.log(edf(o,-10.))-numpy.log(idf(o))) < 10.**-10., 'edf.__call__ w/ tlist set to [to] did not return initial DF'
#also w/ log
assert numpy.fabs(edf(o,[-10.],log=True)-numpy.log(idf(o))) < 10.**-10., 'edf.__call__ w/ tlist set to [to] did not return initial DF (log)'
assert numpy.fabs(edf(o,-10.,log=True)-numpy.log(idf(o))) < 10.**-10., 'edf.__call__ w/ tlist set to [to] did not return initial DF (log)'
# Tests w/ odeint: tlist
codeint= edf(o,[0.,-2.5,-5.,-7.5,-10.],integrate_method='odeint',log=True)
crk6c= edf(o,[0.,-2.5,-5.,-7.5,-10.],integrate_method='rk6_c',log=True)
assert numpy.all(numpy.fabs(codeint-crk6c) < 10.**-4.), 'edf.__call__ w/ odeint and tlist does not give the same result as w/ rk6_c'
# Crazy orbit w/ tlist
crk6c= edf(Orbit([3.,1.,-1.,2.]),[0.],integrate_method='odeint',log=True)
assert crk6c < -20., 'crazy orbit does not have DF equal to zero'
# deriv w/ odeint
codeint= edf(o,[0.,-2.5,-5.,-7.5,-10.],integrate_method='odeint',
deriv='R')
crk6c= edf(o,[0.,-2.5,-5.,-7.5,-10.],integrate_method='rk6_c',deriv='R')
assert numpy.all(numpy.fabs(codeint-crk6c) < 10.**-4.), 'edf.__call__ w/ odeint and tlist does not give the same result as w/ rk6_c (deriv=R)'
# deriv w/ len(tlist)=1
crk6c= edf(o,[0.],integrate_method='rk6_c',deriv='R')
crk6c2= edf(o,0.,integrate_method='rk6_c',deriv='R')
assert numpy.all(numpy.fabs(crk6c-crk6c2) < 10.**-4.), 'edf.__call__ w/ tlist consisting of one time and just a scalar time do not agree'
#Call w/ just to and deriv
assert numpy.fabs(edf(o,-10.,deriv='R')-idf(o)*idf._dlnfdR(o._orb.vxvv[0],o._orb.vxvv[1],o._orb.vxvv[2])) < 10.**-10., 'edf.__call__ w/ to did not return initial DF (deriv=R)'
assert numpy.fabs(edf(o,-10.,deriv='phi')) < 10.**-10., 'edf.__call__ w/ to did not return initial DF (deriv=phi)'
# Call w/ just one t and odeint
codeint= edf(o,0,integrate_method='odeint',log=True)
crk6c= edf(o,0.,integrate_method='rk6_c',log=True)
assert numpy.fabs(codeint-crk6c) < 10.**-4., 'edf.__call__ w/ odeint and tlist does not give the same result as w/ rk6_c'
# Call w/ just one t and fallback to odeint
# turn off C
edf._pot[0].hasC= False
edf._pot[0].hasC_dxdv= False
codeint= edf(o,0,integrate_method='dopr54_c',log=True)
assert numpy.fabs(codeint-crk6c) < 10.**-4., 'edf.__call__ w/ odeint and tlist does not give the same result as w/ rk6_c'
# Call w/ just one t and fallback to leaprog
cleapfrog= edf(o,0,integrate_method='leapfrog_c',log=True)
assert numpy.fabs(cleapfrog-crk6c) < 10.**-4., 'edf.__call__ w/ odeint and tlist does not give the same result as w/ rk6_c'
def test_plot_grid():
idf= dehnendf(beta=0.)
pot= [LogarithmicHaloPotential(normalize=1.),
SteadyLogSpiralPotential(A=-0.005,omegas=0.2)] #very mild non-axi
edf= evolveddiskdf(idf,pot=pot,to=-10.)
mvr, grid= edf.meanvR(0.9,phi=0.2,integrate_method='rk6_c',grid=True,
returnGrid=True,gridpoints=_GRIDPOINTS)
grid.plot()
#w/ list of tiems
mvr, grid= edf.meanvR(0.9,t=[0.,-2.5,-5.,-7.5,-10.],
phi=0.2,integrate_method='rk6_c',grid=True,
returnGrid=True,gridpoints=_GRIDPOINTS)
grid.plot(1)
return None
def test_mildnonaxi_meanvr_grid_tlist():
# Test that for a close to axisymmetric potential, the mean vr is close to zero
idf= dehnendf(beta=0.)
pot= [LogarithmicHaloPotential(normalize=1.),
SteadyLogSpiralPotential(A=-0.005,omegas=0.2)] #very mild non-axi
edf= evolveddiskdf(idf,pot=pot,to=-10.)
mvr, grid= edf.meanvR(0.9,t=[0.,-2.5,-5.,-7.5,-10.],
phi=0.2,integrate_method='rk6_c',
grid=True,returnGrid=True,gridpoints=_GRIDPOINTS)
assert numpy.all(numpy.fabs(mvr) < 0.003), 'meanvR of evolveddiskdf for axisymmetric potential is not equal to zero for list of times'
mvr= edf.meanvR(0.9,t=[0.,-2.5,-5.,-7.5,-10.],
phi=0.2,integrate_method='rk6_c',grid=grid)
assert numpy.all(numpy.fabs(mvr) < 0.003), 'meanvR of evolveddiskdf for axisymmetric potential is not equal to zero when calculated with pre-computed grid for list of times'
return None
def test_meanvT_staeckel_gl():
qdf= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
pot=MWPotential,aA=aAS,cutcounter=True)
from galpy.df import dehnendf #baseline
dfc= dehnendf(profileParams=(1./4.,1.0, 0.2),
beta=0.,correct=False)
#In the mid-plane
vtp9= qdf.meanvT(0.9,0.,gl=True)
assert numpy.fabs(vtp9-dfc.meanvT(0.9)) < 0.05, "qdf's meanvT is not close to that of dehnendf"
assert vtp9 < vcirc(MWPotential,0.9), "qdf's meanvT is not less than the circular velocity (which we expect)"
#higher up
assert qdf.meanvR(0.9,0.2,gl=True) < vtp9, "qdf's meanvT above the plane is not less than in the plane (which we expect)"
assert qdf.meanvR(0.9,-0.25,gl=True) < vtp9, "qdf's meanvT above the plane is not less than in the plane (which we expect)"
return None
def test_mildnonaxi_meanvt_grid():
# Test that for a close to axisymmetric potential, the mean vt is close to that of the initial DF
idf= dehnendf(beta=0.)
pot= [LogarithmicHaloPotential(normalize=1.),
SteadyLogSpiralPotential(A=-0.005,omegas=0.2)] #very mild non-axi
edf= evolveddiskdf(idf,pot=pot,to=-10.)
mvt, grid= edf.meanvT(0.9,phi=0.2,integrate_method='rk6_c',grid=True,
returnGrid=True,gridpoints=_GRIDPOINTS)
assert numpy.fabs(mvt-idf.meanvT(0.9)) < 0.005, 'meanvT of evolveddiskdf for axisymmetric potential is not equal to that of the initial dehnendf'
mvt= edf.meanvT(0.9,phi=0.2,integrate_method='rk6_c',grid=grid,
gridpoints=_GRIDPOINTS,)
assert numpy.fabs(mvt-idf.meanvT(0.9)) < 0.005, 'meanvT of evolveddiskdf for axisymmetric potential is not equal to that of the initial dehnendf when calculated with pre-computed grid'
global _maxi_meanvt
_maxi_meanvt= mvt
return None
def _calc_pred(vs,location,l,dmax):
"""Calculate the predicted distribution"""
thisvs= vs
if _PREDICTCACHE:
savefilename= os.path.join(os.getenv('DATADIR'),'bovy','vclos',
'predictedVclosDistribution_%i_%.0f_%i_%.0f_%.2f_%i_%.2f.sav' % (location,l,len(vs),_PREDICTVC,_PREDICTSIGMA,_PREDICTNDS,dmax))
if _PREDICTCACHE and os.path.exists(savefilename):
#Load prediction
savefile= open(savefilename,'rb')
pred_dist= pickle.load(savefile)
savefile.close()
else:
ds= numpy.linspace(0.,dmax,_PREDICTNDS)
dfc= dehnendf(beta=0.,profileParams=(1./3.,1.,_PREDICTSIGMA),correct=True,niter=20)
thisl= l*_DEGTORAD
thisvsolar= numpy.dot(_PREDICTVSOLAR,numpy.array([-numpy.cos(thisl),numpy.sin(thisl)]))
thisvs+= thisvsolar
thisvs/= _PREDICTVC
ii= 0
pred_dist= numpy.zeros(len(vs))
while ii < len(vs):
out= 0.
norm= 0.
for jj in range(_PREDICTNDS):
d= ds[jj]
R= math.sqrt(1.+d**2.-2.*d*math.cos(thisl))
if R == 0.:
R+= 0.0001
d+= 0.0001
if 1./math.cos(thisl) < d and math.cos(thisl) > 0.:
theta= math.pi-math.asin(d/R*math.sin(thisl))
else:
theta= math.asin(d/R*math.sin(thisl))
vR= -thisvs[ii]*math.cos(theta+thisl) #vperp=0 for marginalization
vT= thisvs[ii]*math.sin(theta+thisl)
o= Orbit([R,vR,vT,theta])
surfmass= dfc.surfacemassLOS(d,thisl,deg=False)
out+= surfmass*dfc(o,marginalizeVperp=True)/dfc.targetSurfacemass(R)
norm+= surfmass
pred_dist[ii]= out/norm
ii+= 1
if _PREDICTCACHE:
savefile= open(savefilename,'wb')
try:
pickle.dump(pred_dist,savefile)
finally:
savefile.close()
return pred_dist
def test_mildnonaxi_oortK_grid():
# Test that for a close to axisymmetric potential, the oortK is close to zero
idf= dehnendf(beta=0.)
pot= [LogarithmicHaloPotential(normalize=1.),
EllipticalDiskPotential(twophio=0.001)] #very mild non-axi
edf= evolveddiskdf(idf,pot=pot,to=-10.)
ok, grid, dgridR, dgridphi=\
edf.oortK(0.9,phi=0.2,integrate_method='rk6_c',
grid=True,derivRGrid=True,derivphiGrid=True,
returnGrids=True,
gridpoints=_GRIDPOINTS,derivGridpoints=_GRIDPOINTS)
assert numpy.fabs(ok) < 0.005, 'oortK of evolveddiskdf for axisymmetric potential is not equal to that of initial DF'
ok= edf.oortK(0.9,phi=0.2,integrate_method='rk6_c',grid=grid,
derivRGrid=dgridR,derivphiGrid=dgridphi,
gridpoints=_GRIDPOINTS,derivGridpoints=_GRIDPOINTS)
assert numpy.fabs(ok) < 0.005, 'oortK of evolveddiskdf for axisymmetric potential is not equal to that of initial DF when calculated with pre-computed grid'
return None
def test_mildnonaxi_vertexdev_grid():
# Test that for a close to axisymmetric potential, the vertex deviation is close to zero
idf= dehnendf(beta=0.)
pot= [LogarithmicHaloPotential(normalize=1.),
SteadyLogSpiralPotential(A=-0.005,omegas=0.2)] #very mild non-axi
edf= evolveddiskdf(idf,pot=pot,to=-10.)
vdev, grid= edf.vertexdev(0.9,phi=0.2,integrate_method='rk6_c',grid=True,
returnGrid=True,gridpoints=_GRIDPOINTS)
assert numpy.fabs(vdev) < 2., 'vertexdev of evolveddiskdf for axisymmetric potential is not close to zero' #2 is pretty big, but the weak spiral creates that
vdev= edf.vertexdev(0.9,phi=0.2,integrate_method='rk6_c',grid=grid,
gridpoints=_GRIDPOINTS)
assert numpy.fabs(vdev) < 2., 'vertexdev of evolveddiskdf for axisymmetric potential is not equal zero when calculated with pre-computed grid'
vdev= edf.vertexdev(0.9,phi=0.2,integrate_method='rk6_c',grid=grid,
sigmaR2=_maxi_sigmar2,sigmaT2=_maxi_sigmat2,
sigmaRT=_maxi_sigmart,gridpoints=_GRIDPOINTS)
assert numpy.fabs(vdev) < 2., 'sigmart of evolveddiskdf for axisymmetric potential is not equal to zero when calculated with pre-computed sigmaR2,sigmaT2,sigmaRT'
return None
def test_mildnonaxi_vertexdev_grid():
# Test that for a close to axisymmetric potential, the vertex deviation is close to zero
idf= dehnendf(beta=0.)
pot= [LogarithmicHaloPotential(normalize=1.),
SteadyLogSpiralPotential(A=-0.005,omegas=0.2)] #very mild non-axi
edf= evolveddiskdf(idf,pot=pot,to=-10.)
vdev, grid= edf.vertexdev(0.9,phi=0.2,integrate_method='rk6_c',grid=True,
returnGrid=True,gridpoints=_GRIDPOINTS)
assert numpy.fabs(vdev) < 2., 'vertexdev of evolveddiskdf for axisymmetric potential is not close to zero' #2 is pretty big, but the weak spiral creates that
vdev= edf.vertexdev(0.9,phi=0.2,integrate_method='rk6_c',grid=grid,
gridpoints=_GRIDPOINTS)
assert numpy.fabs(vdev) < 2., 'vertexdev of evolveddiskdf for axisymmetric potential is not equal zero when calculated with pre-computed grid'
vdev= edf.vertexdev(0.9,phi=0.2,integrate_method='rk6_c',grid=grid,
sigmaR2=_maxi_sigmar2,sigmaT2=_maxi_sigmat2,
sigmaRT=_maxi_sigmart,gridpoints=_GRIDPOINTS)
assert numpy.fabs(vdev) < 2., 'sigmart of evolveddiskdf for axisymmetric potential is not equal to zero when calculated with pre-computed sigmaR2,sigmaT2,sigmaRT'
return None
def test_elliptical_cold_vertexdev():
# Test that the vertex deviations for the elliptical disk behaves as analytically expected
idf= dehnendf(beta=0.,profileParams=(1./3.,1.,0.0125))
cp= 0.05
pot= [LogarithmicHaloPotential(normalize=1.),
EllipticalDiskPotential(cp=cp,sp=0.,p=0.,tform=-150.,tsteady=125.)]
edf= evolveddiskdf(idf,pot=pot,to=-150.)
#Should be -2cp in radians
vdev, grid= edf.vertexdev(0.9,phi=-numpy.pi/4.,
integrate_method='rk6_c',grid=True,nsigma=7.,
returnGrid=True,gridpoints=_GRIDPOINTS)
assert numpy.fabs(vdev/180.*numpy.pi+2.*cp) < 10.**-3., 'Cold elliptical disk does not agree with analytical calculation for vertexdev'
#Should be 0
vdev, grid= edf.vertexdev(0.9,phi=0.,
integrate_method='rk6_c',grid=True,nsigma=7.,
returnGrid=True,gridpoints=_GRIDPOINTS)
assert numpy.fabs(vdev) < 10.**-2., 'Cold elliptical disk does not agree with analytical calculation for vertexdev'
return None
def test_elliptical_cold_vt():
# Test that the rotational velocity for the elliptical disk behaves as analytically expected
idf= dehnendf(beta=0.,profileParams=(1./3.,1.,0.0125))
cp= 0.05
pot= [LogarithmicHaloPotential(normalize=1.),
EllipticalDiskPotential(cp=cp,sp=0.,p=0.,tform=-150.,tsteady=125.)]
edf= evolveddiskdf(idf,pot=pot,to=-150.)
#Should be 1.
mvt, grid= edf.meanvT(0.9,phi=-numpy.pi/4.,
integrate_method='rk6_c',grid=True,
returnGrid=True,gridpoints=_GRIDPOINTS)
assert numpy.fabs(mvt-1.) < 10.**-3., 'Cold elliptical disk does not agree with analytical calculation for vt'
#Should be 1.-cp
mvt, grid= edf.meanvT(0.9,phi=0.,
integrate_method='rk6_c',grid=True,nsigma=7.,
returnGrid=True,gridpoints=_GRIDPOINTS)
assert numpy.fabs(mvt-1.+cp) < 10.**-3., 'Cold elliptical disk does not agree with analytical calculation for vt'
return None
def add_galaxy(self, mass, n_stars, axis, theta, tras):
tras = np.asarray(tras)
R = Universe.rotation_matrix(axis, theta)
dfc = dehnendf(beta=0.)
o = dfc.sample(n=n_stars, returnOrbit=True)
stars = np.zeros(n_stars, dtype=self.__star_fields)
mass = random.randint(1e2, 1e10)
for idx in range(len(stars)):
stars['pos'][idx, 0:2] = [o[idx].x(), o[idx].y()]
stars['mass'][idx] = mass
# Get velocity from angular velocity of sample
tangential = np.linalg.norm(stars['pos'][idx, 0:2]) * o[idx].vT()
unit_pos = stars['pos'][idx, 0:2] / np.linalg.norm(
stars['pos'][idx, 0:2])
unit_vel = np.array([-unit_pos[1], unit_pos[0]
]) # 90 deg rotated counterclockwise
stars['vel'][idx, 0:2] = unit_vel * tangential
stars['pos'][idx] = np.dot(R, stars['pos'][idx]) + tras
self.stars = np.append(self.stars, stars)
def test_mildnonaxi_sigmat2_grid():
# Test that for a close to axisymmetric potential, the sigmaR2 is close to the value of the initial DF
idf= dehnendf(beta=0.)
pot= [LogarithmicHaloPotential(normalize=1.),
SteadyLogSpiralPotential(A=-0.005,omegas=0.2)] #very mild non-axi
edf= evolveddiskdf(idf,pot=pot,to=-10.)
st2, grid= edf.sigmaT2(0.9,phi=0.2,integrate_method='rk6_c',grid=True,
returnGrid=True,gridpoints=_GRIDPOINTS)
ist2= idf.sigmaT2(0.9)
assert numpy.fabs(numpy.log(st2)-numpy.log(ist2)) < 0.025, 'sigmat2 of evolveddiskdf for axisymmetric potential is not equal to that of initial DF'
st2= edf.sigmaT2(0.9,phi=0.2,integrate_method='rk6_c',grid=grid,
gridpoints=_GRIDPOINTS)
assert numpy.fabs(numpy.log(st2)-numpy.log(ist2)) < 0.025, 'sigmat2 of evolveddiskdf for axisymmetric potential is not equal to that of initial DF when calculated with pre-computed grid'
st2= edf.sigmaT2(0.9,phi=0.2,integrate_method='rk6_c',grid=grid,
meanvT=_maxi_meanvt,surfacemass=_maxi_surfacemass,
gridpoints=_GRIDPOINTS)
assert numpy.fabs(numpy.log(st2)-numpy.log(ist2)) < 0.025, 'sigmat2 of evolveddiskdf for axisymmetric potential is not equal to that of initial DF when calculated with pre-computed grid and meanvR,surfacemass'
global _maxi_sigmat2
_maxi_sigmat2= st2
return None
def test_mildnonaxi_sigmart_grid():
# Test that for a close to axisymmetric potential, the sigmaR2 is close to zero
idf= dehnendf(beta=0.)
pot= [LogarithmicHaloPotential(normalize=1.),
SteadyLogSpiralPotential(A=-0.005,omegas=0.2)] #very mild non-axi
edf= evolveddiskdf(idf,pot=pot,to=-10.)
srt, grid= edf.sigmaRT(0.9,phi=0.2,integrate_method='rk6_c',grid=True,
returnGrid=True,gridpoints=_GRIDPOINTS)
assert numpy.fabs(srt) < 0.01, 'sigmart of evolveddiskdf for axisymmetric potential is not equal to zero'
srt= edf.sigmaRT(0.9,phi=0.2,integrate_method='rk6_c',grid=grid,
gridpoints=_GRIDPOINTS)
assert numpy.fabs(srt) < 0.01, 'sigmart of evolveddiskdf for axisymmetric potential is not equal zero when calculated with pre-computed grid'
srt= edf.sigmaRT(0.9,phi=0.2,integrate_method='rk6_c',grid=grid,
meanvR=_maxi_meanvr,
meanvT=_maxi_meanvt,surfacemass=_maxi_surfacemass,
gridpoints=_GRIDPOINTS)
assert numpy.fabs(srt) < 0.01, 'sigmart of evolveddiskdf for axisymmetric potential is not equal to zero when calculated with pre-computed grid and meanvR,surfacemass'
global _maxi_sigmart
_maxi_sigmart= srt
return None
def test_mildnonaxi_sigmart_grid():
# Test that for a close to axisymmetric potential, the sigmaR2 is close to zero
idf= dehnendf(beta=0.)
pot= [LogarithmicHaloPotential(normalize=1.),
SteadyLogSpiralPotential(A=-0.005,omegas=0.2)] #very mild non-axi
edf= evolveddiskdf(idf,pot=pot,to=-10.)
srt, grid= edf.sigmaRT(0.9,phi=0.2,integrate_method='rk6_c',grid=True,
returnGrid=True,gridpoints=_GRIDPOINTS)
assert numpy.fabs(srt) < 0.01, 'sigmart of evolveddiskdf for axisymmetric potential is not equal to zero'
srt= edf.sigmaRT(0.9,phi=0.2,integrate_method='rk6_c',grid=grid,
gridpoints=_GRIDPOINTS)
assert numpy.fabs(srt) < 0.01, 'sigmart of evolveddiskdf for axisymmetric potential is not equal zero when calculated with pre-computed grid'
srt= edf.sigmaRT(0.9,phi=0.2,integrate_method='rk6_c',grid=grid,
meanvR=_maxi_meanvr,
meanvT=_maxi_meanvt,surfacemass=_maxi_surfacemass,
gridpoints=_GRIDPOINTS)
assert numpy.fabs(srt) < 0.01, 'sigmart of evolveddiskdf for axisymmetric potential is not equal to zero when calculated with pre-computed grid and meanvR,surfacemass'
global _maxi_sigmart
_maxi_sigmart= srt
return None
def test_mildnonaxi_sigmat2_grid():
# Test that for a close to axisymmetric potential, the sigmaR2 is close to the value of the initial DF
idf= dehnendf(beta=0.)
pot= [LogarithmicHaloPotential(normalize=1.),
SteadyLogSpiralPotential(A=-0.005,omegas=0.2)] #very mild non-axi
edf= evolveddiskdf(idf,pot=pot,to=-10.)
st2, grid= edf.sigmaT2(0.9,phi=0.2,integrate_method='rk6_c',grid=True,
returnGrid=True,gridpoints=_GRIDPOINTS)
ist2= idf.sigmaT2(0.9)
assert numpy.fabs(numpy.log(st2)-numpy.log(ist2)) < 0.025, 'sigmat2 of evolveddiskdf for axisymmetric potential is not equal to that of initial DF'
st2= edf.sigmaT2(0.9,phi=0.2,integrate_method='rk6_c',grid=grid,
gridpoints=_GRIDPOINTS)
assert numpy.fabs(numpy.log(st2)-numpy.log(ist2)) < 0.025, 'sigmat2 of evolveddiskdf for axisymmetric potential is not equal to that of initial DF when calculated with pre-computed grid'
st2= edf.sigmaT2(0.9,phi=0.2,integrate_method='rk6_c',grid=grid,
meanvT=_maxi_meanvt,surfacemass=_maxi_surfacemass,
gridpoints=_GRIDPOINTS)
assert numpy.fabs(numpy.log(st2)-numpy.log(ist2)) < 0.025, 'sigmat2 of evolveddiskdf for axisymmetric potential is not equal to that of initial DF when calculated with pre-computed grid and meanvR,surfacemass'
global _maxi_sigmat2
_maxi_sigmat2= st2
return None
def test_mildnonaxi_oortA_grid_tlist():
# Test that for a close to axisymmetric potential, the oortA is close to the value of the initial DF
idf= dehnendf(beta=0.)
pot= [LogarithmicHaloPotential(normalize=1.),
EllipticalDiskPotential(twophio=0.001)] #very mild non-axi
edf= evolveddiskdf(idf,pot=pot,to=-10.)
oa, grid, dgridR, dgridphi=\
edf.oortA(0.9,t=[0.,-2.5,-5.,-7.5,-10.],
phi=0.2,integrate_method='rk6_c',
grid=True,derivRGrid=True,derivphiGrid=True,
returnGrids=True,
gridpoints=_GRIDPOINTS,derivGridpoints=_GRIDPOINTS)
ioa= idf.oortA(0.9)
assert numpy.all(numpy.fabs(oa-ioa) < 0.005), 'oortA of evolveddiskdf for axisymmetric potential is not equal to that of initial DF'
oa= edf.oortA(0.9,t=[0.,-2.5,-5.,-7.5,-10.],
phi=0.2,integrate_method='rk6_c',grid=grid,
derivRGrid=dgridR,derivphiGrid=dgridphi,
gridpoints=_GRIDPOINTS,derivGridpoints=_GRIDPOINTS)
assert numpy.all(numpy.fabs(oa-ioa) < 0.005), 'oortA of evolveddiskdf for axisymmetric potential is not equal to that of initial DF when calculated with pre-computed grid'
return None
def determine_vsolar_error(spiral=False):
#Read the APOGEE-RC data and pixelate it
#APOGEE-RC
data= apread.rcsample()
if _ADDLLOGGCUT:
data= data[data['ADDL_LOGG_CUT'] == 1]
#Cut
indx= (numpy.fabs(data['RC_GALZ']) < 0.25)*(data['METALS'] > -1000.)
data= data[indx]
#Set up the Dehnen DF that we will sample from
dfc= dehnendf(beta=0.,profileParams=(3./8.,33.3,31.4/220.))
trueVsolar= 24./220.
ntrials= 100
mockvsolars= []
for ii in range(ntrials):
print "Working on %i / %i" % (ii+1,ntrials)
mockvsolars.append(determine_vsolar_mock(data,dfc,trueVsolar,spiral))
mockvsolars= numpy.array(mockvsolars)
print mockvsolars
print numpy.mean(mockvsolars-24.), numpy.std(mockvsolars)
print numpy.sum(numpy.fabs(mockvsolars-24.) > 1.)/float(ntrials)
return None
def test_call_marginalizevperp():
from galpy.orbit import Orbit
idf= dehnendf(beta=0.)
pot= [LogarithmicHaloPotential(normalize=1.),
EllipticalDiskPotential(twophio=0.001)] #very mild non-axi
edf= evolveddiskdf(idf,pot=pot[0],to=-10.) #one with just one potential
#l=0
R,phi,vR = 0.8, 0., 0.4
vts= numpy.linspace(0.,1.5,51)
pvts= numpy.array([edf(Orbit([R,vR,vt,phi]),integrate_method='rk6_c') for vt in vts])
assert numpy.fabs(numpy.sum(pvts)*(vts[1]-vts[0])\
-edf(Orbit([R,vR,0.,phi]),marginalizeVperp=True,integrate_method='rk6_c')) < 10.**-3.5, 'evolveddiskdf call w/ marginalizeVperp does not work'
#l=270
edf= evolveddiskdf(idf,pot=pot,to=-10.)
R,phi,vT = numpy.sin(numpy.pi/6.), -numpy.pi/3.,0.7 #l=30 degree
vrs= numpy.linspace(-1.,1.,101)
pvrs= numpy.array([edf(Orbit([R,vr,vT,phi]),integrate_method='rk6_c') for vr in vrs])
assert numpy.fabs(numpy.log(numpy.sum(pvrs)*(vrs[1]-vrs[0]))\
-edf(Orbit([R,0.,vT,phi]),
marginalizeVperp=True,
integrate_method='rk6_c',log=True,
nsigma=4)) < 10.**-2.5, 'evolveddiskdf call w/ marginalizeVperp does not work'
return None
def test_call_marginalizevlos():
from galpy.orbit import Orbit
idf= dehnendf(beta=0.)
pot= [LogarithmicHaloPotential(normalize=1.),
EllipticalDiskPotential(twophio=0.001)] #very mild non-axi
edf= evolveddiskdf(idf,pot=pot[0],to=-10.) #one with just one potential
#l=0
R,phi,vT = 0.8, 0., 0.7
vrs= numpy.linspace(-1.,1.,101)
pvrs= numpy.array([edf(Orbit([R,vr,vT,phi]),integrate_method='rk6_c') for vr in vrs])
assert numpy.fabs(numpy.log(numpy.sum(pvrs)*(vrs[1]-vrs[0]))\
-edf(Orbit([R,0.,vT,phi]),marginalizeVlos=True,integrate_method='rk6_c',log=True)) < 10.**-4., 'diskdf call w/ marginalizeVlos does not work'
#l=270, this DF has some issues, but it suffices to test the mechanics of the code
edf= evolveddiskdf(idf,pot=pot,to=-10.)
R,phi,vR = numpy.sin(numpy.pi/6.), -numpy.pi/3.,0.4 #l=30 degree
vts= numpy.linspace(0.3,1.5,101)
pvts= numpy.array([edf(Orbit([R,vR,vt,phi]),integrate_method='rk6_c') for vt in vts])
assert numpy.fabs(numpy.sum(pvts)*(vts[1]-vts[0])\
-edf(Orbit([R,vR,0.,phi]),
marginalizeVlos=True,
integrate_method='rk6_c',
nsigma=4)) < 10.**-3.5, 'diskdf call w/ marginalizeVlos does not work'
return None
def test_diskdf():
from galpy.df import dehnendf
# Init. dehnendf w/ flat rot., hr=1/3, hs=1, and sr(1)=0.2
df = dehnendf(beta=0., profileParams=(1. / 3., 1.0, 0.2))
# Same, w/ correction factors to scale profiles
dfc = dehnendf(beta=0.,
profileParams=(1. / 3., 1.0, 0.2),
correct=True,
niter=20)
if True:
# Log. diff. between scale and DF surf. dens.
numpy.log(df.surfacemass(0.5) / df.targetSurfacemass(0.5))
assert numpy.fabs(
numpy.log(df.surfacemass(0.5) / df.targetSurfacemass(0.5)) +
0.056954077791649592
) < 10.**-4., 'diskdf does not behave as expected'
# Same for corrected DF
numpy.log(dfc.surfacemass(0.5) / dfc.targetSurfacemass(0.5))
assert numpy.fabs(
numpy.log(dfc.surfacemass(0.5) / dfc.targetSurfacemass(0.5)) +
4.1440377205802041e-06
) < 10.**-4., 'diskdf does not behave as expected'
# Log. diff between scale and DF sr
numpy.log(df.sigmaR2(0.5) / df.targetSigma2(0.5))
assert numpy.fabs(
numpy.log(df.sigmaR2(0.5) / df.targetSigma2(0.5)) +
0.12786083001363127
) < 10.**-4., 'diskdf does not behave as expected'
# Same for corrected DF
numpy.log(dfc.sigmaR2(0.5) / dfc.targetSigma2(0.5))
assert numpy.fabs(
numpy.log(dfc.sigmaR2(0.5) / dfc.targetSigma2(0.5)) +
6.8065001252214986e-06
) < 10.**-4., 'diskdf does not behave as expected'
# Evaluate DF w/ R,vR,vT
df(numpy.array([0.9, 0.1, 0.8]))
assert numpy.fabs(
df(numpy.array([0.9, 0.1, 0.8])) - numpy.array(0.1740247246180417)
) < 10.**-4., 'diskdf does not behave as expected'
# Evaluate corrected DF w/ Orbit instance
from galpy.orbit import Orbit
dfc(Orbit([0.9, 0.1, 0.8]))
assert numpy.fabs(
dfc(Orbit([0.9, 0.1, 0.8])) - numpy.array(0.16834863725552207)
) < 10.**-4., 'diskdf does not behave as expected'
# Calculate the mean velocities
df.meanvR(0.9), df.meanvT(0.9)
assert numpy.fabs(
df.meanvR(0.9)) < 10.**-4., 'diskdf does not behave as expected'
assert numpy.fabs(df.meanvT(0.9) - 0.91144428051168291
) < 10.**-4., 'diskdf does not behave as expected'
# Calculate the velocity dispersions
numpy.sqrt(dfc.sigmaR2(0.9)), numpy.sqrt(dfc.sigmaT2(0.9))
assert numpy.fabs(numpy.sqrt(dfc.sigmaR2(0.9)) - 0.22103383792719539
) < 10.**-4., 'diskdf does not behave as expected'
assert numpy.fabs(numpy.sqrt(dfc.sigmaT2(0.9)) - 0.17613725303902811
) < 10.**-4., 'diskdf does not behave as expected'
# Calculate the skew of the velocity distribution
df.skewvR(0.9), df.skewvT(0.9)
assert numpy.fabs(
df.skewvR(0.9)) < 10.**-4., 'diskdf does not behave as expected'
assert numpy.fabs(df.skewvT(0.9) + 0.47331638366025863
) < 10.**-4., 'diskdf does not behave as expected'
# Calculate the kurtosis of the velocity distribution
df.kurtosisvR(0.9), df.kurtosisvT(0.9)
assert numpy.fabs(df.kurtosisvR(0.9) + 0.13561300880237059
) < 10.**-4., 'diskdf does not behave as expected'
assert numpy.fabs(df.kurtosisvT(0.9) - 0.12612702099300721
) < 10.**-4., 'diskdf does not behave as expected'
# Calculate a higher-order moment of the velocity DF
df.vmomentsurfacemass(1., 6., 2.) / df.surfacemass(1.)
assert numpy.fabs(
df.vmomentsurfacemass(1., 6., 2.) / df.surfacemass(1.) -
0.00048953492205559054
) < 10.**-4., 'diskdf does not behave as expected'
# Calculate the Oort functions
dfc.oortA(1.), dfc.oortB(1.), dfc.oortC(1.), dfc.oortK(1.)
assert numpy.fabs(dfc.oortA(1.) - 0.40958989067012197
) < 10.**-4., 'diskdf does not behave as expected'
assert numpy.fabs(dfc.oortB(1.) + 0.49396172114486514
) < 10.**-4., 'diskdf does not behave as expected'
assert numpy.fabs(
dfc.oortC(1.)) < 10.**-4., 'diskdf does not behave as expected'
assert numpy.fabs(
dfc.oortK(1.)) < 10.**-4., 'diskdf does not behave as expected'
# Sample Orbits from the DF, returns list of Orbits
numpy.random.seed(1)
os = dfc.sample(n=100, returnOrbit=True, nphi=1)
# check that these have the right mean radius = 2hr=2/3
rs = numpy.array([o.R() for o in os])
assert numpy.fabs(numpy.mean(rs) - 2. / 3.) < 0.1
# Sample vR and vT at given R, check their mean
vrvt = dfc.sampleVRVT(0.7, n=500, target=True)
vt = vrvt[:, 1]
assert numpy.fabs(numpy.mean(vrvt[:, 0])) < 0.05
assert numpy.fabs(numpy.mean(vt) - dfc.meanvT(0.7)) < 0.01
# Sample Orbits along a given line-of-sight
os = dfc.sampleLOS(45., n=1000)
return None
elif dmaxs:
pred_file.close()
pred_file= os.path.join(os.getenv('DATADIR'),'bovy','vclos',
'l_vhelio_dmax_0.625000.sav')
pred_file= open(pred_file,'rb')
ls= pickle.load(pred_file)
avg_pred2= pickle.load(pred_file)
pred_file.close()
pred_file= os.path.join(os.getenv('DATADIR'),'bovy','vclos',
'l_vhelio_dmax_1.875000.sav')
pred_file= open(pred_file,'rb')
ls= pickle.load(pred_file)
avg_pred3= pickle.load(pred_file)
pred_file.close()
else:
dfc= dehnendf(beta=0.,correct=True,niter=20)
if betas:
dfc2= dehnendf(beta=0.1,correct=True,niter=20)
dfc3= dehnendf(beta=-0.1,correct=True,niter=20)
nds= 51
nls= 101
ds= numpy.linspace(0.,10./8.,nds)
ls= numpy.linspace(0.,360.,nls)
avg_pred= numpy.zeros(nls)
if betas:
avg_pred2= numpy.zeros(nls)
avg_pred3= numpy.zeros(nls)
for ii in range(nls):
meanvlos= 0.
norm= 0.
if betas:
from amuse.lab import *
from galpy.df import quasiisothermaldf, dehnendf
from galpy.potential import MWPotential2014, to_amuse
from galpy.util import bovy_conversion
from galpy.actionAngle import actionAngleStaeckel
import numpy as np
aAS = actionAngleStaeckel(pot=MWPotential2014, delta=0.45, c=True)
qdfS = quasiisothermaldf(1./3., 0.2, 0.1, 1., 1., pot=MWPotential2014, aA=aAS, cutcounter=True)
dfc = dehnendf(beta=0.)
'''
sample from Dehnen distribution function to get cylindrical coordinates
where |r| <= 1.0 kpc
'''
nsamples = 4000
o = dfc.sample(n=nsamples, returnOrbit=True, nphi=1, rrange=[0.0, 1.0])
rvals_spherical = [ e.R() for e in o ]
thetavals_spherical = [ np.arccos(1 - 2*np.random.random()) for i in range(nsamples) ]
rvals_cylindrical = [ rvals_spherical[i]*np.sin(thetavals_spherical[i]) for i in range(nsamples) ]
phivals_cylindrical = 2*np.pi * np.random.random(nsamples)
zvals_cylindrical = [ rvals_spherical[i]*np.cos(thetavals_spherical[i]) for i in range(nsamples) ]
#using Staeckel
aAS = actionAngleStaeckel(pot=MWPotential2014, delta=0.45, c=True)
qdfS = quasiisothermaldf(1./3., 0.2, 0.1, 1., 1., pot=MWPotential2014, aA=aAS, cutcounter=True)
#Quick def
def safe_dl_to_rphi(d,l):
R= math.sqrt(1.+d**2.-2.*d*math.cos(l))
if R == 0.:
R= 0.0001
d+= 0.0001
if 1./math.cos(l) < d and math.cos(l) > 0.:
theta= math.pi-math.asin(d/R*math.sin(l))
else:
theta= math.asin(d/R*math.sin(l))
return (R,theta,d,l)
if __name__ == '__main__':
nds= 101
ds= numpy.linspace(0.001,8./8.,nds)
dfc= dehnendf(profileParams=(3./8.,2.,36./218.),beta=0.,correct=False)
if False:
#First calculate mean
mean_savefilename= 'vhelio_l90.sav'
if os.path.exists(mean_savefilename):
savefile= open(mean_savefilename,'rb')
plotds= pickle.load(savefile)
plotvlos= pickle.load(savefile)
savefile.close()
else:
vlos= numpy.zeros(nds)
for jj in range(nds):
print jj
d,l= ds[jj], 90./180.*numpy.pi
R,theta,d,l= safe_dl_to_rphi(d,l)
vlos[jj]= dfc.meanvT(R)*math.sin(theta+l)
