def test_sigmalos_wlog_zerobeta():
from galpy.potential import LogarithmicHaloPotential
lp= LogarithmicHaloPotential(normalize=1.,q=1.)
rs= numpy.linspace(0.5,2.,3)
assert numpy.all(numpy.fabs(numpy.array([jeans.sigmalos(lp,r) for r in rs])-1./numpy.sqrt(2.)) < 1e-8), 'Radial sigma_los computed w/ spherical Jeans equation incorrect for LogarithmicHaloPotential and beta=0'
# Also with pre-computed sigmar
rs= numpy.linspace(0.5,2.,11)
# beta = 0 --> sigma = vc/sqrt(2)
assert numpy.all(numpy.fabs(numpy.array([jeans.sigmalos(lp,r,sigma_r=1./numpy.sqrt(2.)) for r in rs])-1./numpy.sqrt(2.)) < 1e-8), 'Radial sigma_los computed w/ spherical Jeans equation incorrect for LogarithmicHaloPotential and beta=0'
# Also with pre-computed, callable sigmar
rs= numpy.linspace(0.5,2.,11)
# beta = 0 --> sigma = vc/sqrt(2)
assert numpy.all(numpy.fabs(numpy.array([jeans.sigmalos(lp,r,sigma_r=lambda x: 1./numpy.sqrt(2.)) for r in rs])-1./numpy.sqrt(2.)) < 1e-8), 'Radial sigma_los computed w/ spherical Jeans equation incorrect for LogarithmicHaloPotential and beta=0'
# Also with pre-computed, callable sigmar and dens given
rs= numpy.linspace(0.5,2.,11)
# beta = 0 --> sigma = vc/sqrt(2)
assert numpy.all(numpy.fabs(numpy.array([jeans.sigmalos(lp,r,dens=lambda x: x**-2,sigma_r=lambda x: 1./numpy.sqrt(2.)) for r in rs])-1./numpy.sqrt(2.)) < 1e-8), 'Radial sigma_los computed w/ spherical Jeans equation incorrect for LogarithmicHaloPotential and beta=0'
# Also with pre-computed, callable sigmar and dens,surfdens given as func
rs= numpy.linspace(0.5,2.,11)
# beta = 0 --> sigma = vc/sqrt(2)
assert numpy.all(numpy.fabs(numpy.array([jeans.sigmalos(lp,r,dens=lambda x: lp.dens(x,0.),surfdens=lambda x: lp.surfdens(x,numpy.inf),sigma_r=lambda x: 1./numpy.sqrt(2.)) for r in rs])-1./numpy.sqrt(2.)) < 1e-8), 'Radial sigma_los computed w/ spherical Jeans equation incorrect for LogarithmicHaloPotential and beta=0'
# Also with pre-computed, callable sigmar and dens,surfdens given (value)
rs= numpy.linspace(0.5,2.,11)
# beta = 0 --> sigma = vc/sqrt(2)
assert numpy.all(numpy.fabs(numpy.array([jeans.sigmalos(lp,r,dens=lambda x: lp.dens(x,0.),surfdens=lp.surfdens(r,numpy.inf),sigma_r=lambda x: 1./numpy.sqrt(2.)) for r in rs])-1./numpy.sqrt(2.)) < 1e-8), 'Radial sigma_los computed w/ spherical Jeans equation incorrect for LogarithmicHaloPotential and beta=0'
# Also with pre-computed sigmar and callable beta
rs= numpy.linspace(0.5,2.,11)
# beta = 0 --> sigma = vc/sqrt(2)
assert numpy.all(numpy.fabs(numpy.array([jeans.sigmalos(lp,r,sigma_r=1./numpy.sqrt(2.),beta=lambda x: 0.) for r in rs])-1./numpy.sqrt(2.)) < 1e-8), 'Radial sigma_los computed w/ spherical Jeans equation incorrect for LogarithmicHaloPotential and beta=0'
return None
def test_TimeInterpPotential():
#Just to check that the code above has run properly
from galpy.potential import LogarithmicHaloPotential, \
MiyamotoNagaiPotential
lp= LogarithmicHaloPotential(normalize=1.)
mp= MiyamotoNagaiPotential(normalize=1.)
tip= TimeInterpPotential(lp,mp)
assert numpy.fabs(tip.Rforce(1.,0.1,t=10.)-lp.Rforce(1.,0.1)) < 10.**-8., 'TimeInterPotential does not work as expected'
assert numpy.fabs(tip.Rforce(1.,0.1,t=200.)-mp.Rforce(1.,0.1)) < 10.**-8., 'TimeInterPotential does not work as expected'
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 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_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_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_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_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_meanvt_grid_rmEstimates():
# Test vmomentsurfacemass w/o having the _estimateX functions in the intial DF
class fakeDehnen(dehnendf): #class that removes the _estimate functions
def __init__(self, *args, **kwargs):
dehnendf.__init__(self, *args, **kwargs)
_estimatemeanvR = property()
_estimatemeanvT = property()
_estimateSigmaR2 = property()
_estimateSigmaT2 = property()
idf = fakeDehnen(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'
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 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_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_sigmar_wlog_constbeta_diffdens_powerlaw():
from galpy.potential import LogarithmicHaloPotential
lp = LogarithmicHaloPotential(normalize=1., q=1.)
rs = numpy.linspace(0.001, 5., 101)
# general beta and r^-gamma --> sigma = vc/sqrt(gamma-2beta)
gamma, beta = 1., 0.
assert numpy.all(
numpy.fabs(
numpy.array([
jeans.sigmar(lp, r, beta=beta, dens=lambda r: r**-gamma)
for r in rs
]) - 1. / numpy.sqrt(gamma - 2. * beta)) < 1e-10
), 'Radial sigma computed w/ spherical Jeans equation incorrect for LogarithmicHaloPotential, beta=0, and power-law density r^-1'
gamma, beta = 3., 0.5
assert numpy.all(
numpy.fabs(
numpy.array([
jeans.sigmar(lp, r, beta=beta, dens=lambda r: r**-gamma)
for r in rs
]) - 1. / numpy.sqrt(gamma - 2. * beta)) < 1e-10
), 'Radial sigma computed w/ spherical Jeans equation incorrect for LogarithmicHaloPotential, beta=0.5, and power-law density r^-3'
gamma, beta = 0., -0.5
assert numpy.all(
numpy.fabs(
numpy.array([
jeans.sigmar(lp, r, beta=beta, dens=lambda r: r**-gamma)
for r in rs
]) - 1. / numpy.sqrt(gamma - 2. * beta)) < 1e-10
), 'Radial sigma computed w/ spherical Jeans equation incorrect for LogarithmicHaloPotential, beta=-0.5, and power-law density r^0'
return None
def test_sigmar_wlog_linbeta():
# for log halo, dens ~ r^-gamma, and beta = -b x r -->
# sigmar = vc sqrt( scipy.special.gamma(-gamma)*scipy.special.gammaincc(-gamma,2*b*r)/[(2*b*r)**-gamma*exp(-2*b*r)]
from scipy import special
from galpy.potential import LogarithmicHaloPotential
lp = LogarithmicHaloPotential(normalize=1., q=1.)
rs = numpy.linspace(0.001, 5., 101)
gamma, b = -0.1, 3.
assert numpy.all(
numpy.fabs(
numpy.array([
jeans.sigmar(
lp, r, beta=lambda x: -b * x, dens=lambda x: x**-gamma) -
numpy.sqrt(
special.gamma(-gamma) *
special.gammaincc(-gamma, 2 * b * r) /
((2 * b * r)**-gamma * numpy.exp(-2. * b * r))) for r in rs
])) < 1e-10
), 'Radial sigma computed w/ spherical Jeans equation incorrect for LogarithmicHaloPotential, beta= -b*r, and dens ~ r^-gamma'
gamma, b = -0.5, 4.
assert numpy.all(
numpy.fabs(
numpy.array([
jeans.sigmar(
lp, r, beta=lambda x: -b * x, dens=lambda x: x**-gamma) -
numpy.sqrt(
special.gamma(-gamma) *
special.gammaincc(-gamma, 2 * b * r) /
((2 * b * r)**-gamma * numpy.exp(-2. * b * r))) for r in rs
])) < 1e-10
), 'Radial sigma computed w/ spherical Jeans equation incorrect for LogarithmicHaloPotential, beta= -b*r, and dens ~ r^-gamma'
return None
def test_impulse_deltav_plummerstream_curved_subhalo_perpendicular():
from galpy.util import conversion
from galpy.potential import LogarithmicHaloPotential
from galpy.df import impulse_deltav_plummer_curvedstream, \
impulse_deltav_plummerstream_curvedstream
R0, V0= 8., 220.
lp= LogarithmicHaloPotential(normalize=1.,q=0.9)
tol= -5.
GM= 10.**-2./conversion.mass_in_1010msol(V0,R0)
rs= 0.625/R0
dt= 0.01*rs/(numpy.pi/4.)
kick= impulse_deltav_plummer_curvedstream(\
numpy.array([[.5,0.1,0.2]]),
numpy.array([[1.2,0.,0.]]),
rs,
numpy.array([0.1,numpy.pi/4.,0.1]),
numpy.array([1.2,0.,0.]),
numpy.array([.5,0.1,0.2]),
GM,rs)
stream_kick= impulse_deltav_plummerstream_curvedstream(\
numpy.array([[.5,0.1,0.2]]),
numpy.array([[1.2,0.,0.]]),
numpy.array([0.]),
rs,
numpy.array([0.1,numpy.pi/4.,0.1]),
numpy.array([1.2,0.,0.]),
numpy.array([.5,0.1,0.2]),
lambda t: GM/dt,rs,lp,-dt/2.,dt/2.)
# Should be equal
assert numpy.all(numpy.fabs((kick-stream_kick)/kick) < 10.**tol), 'Curved, short Plummer-stream kick does not agree with curved Plummer-sphere kick by %g' % (numpy.amax(numpy.fabs((kick-stream_kick)/kick)))
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_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_actionAngleTorus_interppot_freqs():
from galpy.actionAngle import actionAngleTorus
from galpy.potential import LogarithmicHaloPotential, interpRZPotential
lp = LogarithmicHaloPotential(normalize=1.)
ip = interpRZPotential(RZPot=lp,
interpPot=True,
interpDens=True,
interpRforce=True,
interpzforce=True,
enable_c=True)
aAT = actionAngleTorus(pot=lp)
aATi = actionAngleTorus(pot=ip)
jr, jphi, jz = 0.05, 1.1, 0.02
om = aAT.Freqs(jr, jphi, jz)
omi = aATi.Freqs(jr, jphi, jz)
assert numpy.fabs(
(om[0] - omi[0]) / om[0]
) < 0.2, 'Radial frequency computed using the torus machine does not agree between potential and interpolated potential'
assert numpy.fabs(
(om[1] - omi[1]) / om[1]
) < 0.2, 'Azimuthal frequency computed using the torus machine does not agree between potential and interpolated potential'
assert numpy.fabs(
(om[2] - omi[2]) / om[2]
) < 0.8, 'Vertical frequency computed using the torus machine does not agree between potential and interpolated potential'
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_trailingwleadingimpact_error():
#Imports
from galpy.df import streamgapdf
from galpy.orbit import Orbit
from galpy.potential import LogarithmicHaloPotential
from galpy.actionAngle import actionAngleIsochroneApprox
from galpy.util import conversion #for unit conversions
lp = LogarithmicHaloPotential(normalize=1., q=0.9)
aAI = actionAngleIsochroneApprox(pot=lp, b=0.8)
prog_unp_peri = Orbit([
2.6556151742081835, 0.2183747276300308, 0.67876510797240575,
-2.0143395648974671, -0.3273737682604374, 0.24218273922966019
])
V0, R0 = 220., 8.
sigv = 0.365 * (10. / 2.)**(1. / 3.) # km/s
with pytest.raises(ValueError) as excinfo:
dum= streamgapdf(sigv/V0,progenitor=prog_unp_peri,pot=lp,aA=aAI,
leading=False,nTrackChunks=26,
nTrackIterations=1,
sigMeanOffset=4.5,
tdisrupt=10.88\
/conversion.time_in_Gyr(V0,R0),
Vnorm=V0,Rnorm=R0,
impactb=0.,
subhalovel=numpy.array([6.82200571,132.7700529,
149.4174464])/V0,
timpact=0.88/conversion.time_in_Gyr(V0,R0),
impact_angle=2.34,
GM=10.**-2.\
/conversion.mass_in_1010msol(V0,R0),
rs=0.625/R0)
return None
def test_actionAngleTorus_AutoFitWarning():
from galpy.potential import LogarithmicHaloPotential
from galpy.actionAngle import actionAngleTorus
lp= LogarithmicHaloPotential(normalize=1.,q=0.9)
aAT= actionAngleTorus(pot=lp,tol=10.**-8.)
# These should give warnings
jr, jp, jz= 0.27209033, 1.80253892, 0.6078445
ar, ap, az= numpy.array([1.95732492]), numpy.array([6.16753224]), \
numpy.array([4.08233059])
#Turn warnings into errors to test for them
import warnings
with warnings.catch_warnings(record=True) as w:
if PY2: reset_warning_registry('galpy')
warnings.simplefilter("always",galpyWarning)
aAT(jr,jp,jz,ar,ap,az)
# Should raise warning bc of Autofit, might raise others
raisedWarning= False
for wa in w:
raisedWarning= (str(wa.message) == "actionAngleTorus' AutoFit exited with non-zero return status -3: Fit failed the goal by more than 2")
if raisedWarning: break
assert raisedWarning, "actionAngleTorus with flattened LogarithmicHaloPotential and a particular orbit should have thrown a warning, but didn't"
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always",galpyWarning)
aAT.xvFreqs(jr,jp,jz,ar,ap,az)
# Should raise warning bc of Autofit, might raise others
raisedWarning= False
for wa in w:
raisedWarning= (str(wa.message) == "actionAngleTorus' AutoFit exited with non-zero return status -3: Fit failed the goal by more than 2")
if raisedWarning: break
assert raisedWarning, "actionAngleTorus with flattened LogarithmicHaloPotential and a particular orbit should have thrown a warning, but didn't"
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always",galpyWarning)
aAT.Freqs(jr,jp,jz)
# Should raise warning bc of Autofit, might raise others
raisedWarning= False
for wa in w:
raisedWarning= (str(wa.message) == "actionAngleTorus' AutoFit exited with non-zero return status -3: Fit failed the goal by more than 2")
if raisedWarning: break
assert raisedWarning, "actionAngleTorus with flattened LogarithmicHaloPotential and a particular orbit should have thrown a warning, but didn't"
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always",galpyWarning)
aAT.hessianFreqs(jr,jp,jz)
# Should raise warning bc of Autofit, might raise others
raisedWarning= False
for wa in w:
raisedWarning= (str(wa.message) == "actionAngleTorus' AutoFit exited with non-zero return status -3: Fit failed the goal by more than 2")
if raisedWarning: break
assert raisedWarning, "actionAngleTorus with flattened LogarithmicHaloPotential and a particular orbit should have thrown a warning, but didn't"
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always",galpyWarning)
aAT.xvJacobianFreqs(jr,jp,jz,ar,ap,az)
# Should raise warning bc of Autofit, might raise others
raisedWarning= False
for wa in w:
raisedWarning= (str(wa.message) == "actionAngleTorus' AutoFit exited with non-zero return status -3: Fit failed the goal by more than 2")
if raisedWarning: break
assert raisedWarning, "actionAngleTorus with flattened LogarithmicHaloPotential and a particular orbit should have thrown a warning, but didn't"
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_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_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 setup_gd1model(leading=True,
timpact=None,
hernquist=True,
age=9.,
singleImpact=False,
length_factor=1.,
**kwargs):
lp = LogarithmicHaloPotential(normalize=1., q=0.9)
aAI = actionAngleIsochroneApprox(pot=lp, b=0.8)
obs = Orbit([
1.56148083, 0.35081535, -1.15481504, 0.88719443, -0.47713334,
0.12019596
])
sigv = 0.365 / 2. * (9. / age) #km/s, /2 bc tdis x2, adjust for diff. age
if timpact is None:
sdf = streamdf(sigv / 220.,
progenitor=obs,
pot=lp,
aA=aAI,
leading=leading,
nTrackChunks=11,
tdisrupt=age / bovy_conversion.time_in_Gyr(V0, R0),
Vnorm=V0,
Rnorm=R0)
elif singleImpact:
sdf = streamgapdf(sigv / 220.,
progenitor=obs,
pot=lp,
aA=aAI,
leading=leading,
nTrackChunks=11,
tdisrupt=age / bovy_conversion.time_in_Gyr(V0, R0),
Vnorm=V0,
Rnorm=R0,
timpact=timpact,
spline_order=3,
hernquist=hernquist,
**kwargs)
else:
sdf = streampepperdf(sigv / 220.,
progenitor=obs,
pot=lp,
aA=aAI,
leading=leading,
nTrackChunks=101,
tdisrupt=age /
bovy_conversion.time_in_Gyr(V0, R0),
Vnorm=V0,
Rnorm=R0,
timpact=timpact,
spline_order=1,
hernquist=hernquist,
length_factor=length_factor)
sdf.turn_physical_off()
return sdf
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_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_setup_diffsetups():
#Test the different ways to setup a qdf object
#Test errors
try:
qdf= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
aA=aAS,cutcounter=True)
except IOError: pass
else: raise AssertionError("qdf setup w/o pot set did not raise exception")
try:
qdf= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
pot=MWPotential,cutcounter=True)
except IOError: pass
else: raise AssertionError("qdf setup w/o aA set did not raise exception")
from galpy.potential import LogarithmicHaloPotential
try:
qdf= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
pot=LogarithmicHaloPotential(),
aA=aAS,cutcounter=True)
except IOError: pass
else: raise AssertionError("qdf setup w/ aA potential different from pot= did not raise exception")
#qdf setup with an actionAngleIsochrone instance (issue #190)
from galpy.potential import IsochronePotential
from galpy.actionAngle import actionAngleIsochrone
ip= IsochronePotential(normalize=1.,b=2.)
try:
qdf= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
pot=ip,
aA=actionAngleIsochrone(ip=ip),cutcounter=True)
except:
raise
raise AssertionError('quasi-isothermaldf setup w/ an actionAngleIsochrone instance failed')
#qdf setup with an actionAngleIsochrone instance should raise error if potentials are not the same
ip= IsochronePotential(normalize=1.,b=2.)
try:
qdf= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
pot=ip,
aA=actionAngleIsochrone(ip=IsochronePotential(normalize=1.,b=2.5)),
cutcounter=True)
except IOError: pass
else: raise AssertionError("qdf setup w/ aA potential different from pot= did not raise exception")
#precompute
qdf= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
pot=MWPotential,aA=aAS,cutcounter=True,
_precomputerg=True)
qdfnpc= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
pot=MWPotential,aA=aAS,cutcounter=True,
_precomputerg=False)
assert numpy.fabs(qdf.rg(1.1)-qdfnpc.rg(1.1)) < 10.**-5., 'rg calculated from qdf instance w/ precomputerg set is not the same as that computed from an instance w/o it set'
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