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_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_oortC_grid():
# Test that for a close to axisymmetric potential, the oortC 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.)
oc, grid, dgridR, dgridphi=\
edf.oortC(0.9,phi=0.2,integrate_method='rk6_c',
grid=True,derivRGrid=True,derivphiGrid=True,
returnGrids=True,
gridpoints=_GRIDPOINTS,derivGridpoints=_GRIDPOINTS)
assert numpy.fabs(
oc
) < 0.005, 'oortC of evolveddiskdf for axisymmetric potential is not equal to that of initial DF'
oc = edf.oortC(0.9,
phi=0.2,
integrate_method='rk6_c',
grid=grid,
derivRGrid=dgridR,
derivphiGrid=dgridphi,
gridpoints=_GRIDPOINTS,
derivGridpoints=_GRIDPOINTS)
assert numpy.fabs(
oc
) < 0.005, 'oortC of evolveddiskdf for axisymmetric potential is not equal to that of initial DF when calculated with pre-computed grid'
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_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_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_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_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_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_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_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_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_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_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():
# 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_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_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_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_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_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_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_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 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_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_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_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 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_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_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_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
oortB_array = np.empty(len(omegas))
oortC_array = np.empty(len(omegas))
oortK_array = np.empty(len(omegas))
for k in range(len(omegas)):
sp = potential.SpiralArmsPotential(amp=amp30,
N=N,
alpha=alpha,
H=H,
Rs=Rs,
omega=omegas[k],
phi_ref=phi_ref)
sp = potential.DehnenSmoothWrapperPotential(pot=sp,
tform=tform,
tsteady=tsteady)
edf = evolveddiskdf(dfc, [lp, sp], to=sp._tform)
oortA_array[k], grid, derivRGrid, derivphiGrid = edf.oortA(
R=1,
phi=0,
gridpoints=n,
derivGridpoints=n,
grid=True,
derivphiGrid=True,
derivRGrid=True,
nsigma=6.,
returnGrids=True)
oortB_array[k] = edf.oortB(R=1,
phi=0,
grid=grid,
derivphiGrid=derivphiGrid,
def pickle_data(filename, data):
output = open(filename, 'wb')
pickle.dump(data, output)
output.close()
tform = -10 * u.Gyr
vel_disp = 40 / 220
lp = potential.LogarithmicHaloPotential(amp=1, normalize=1)
dfc = dehnendf(beta=0,
profileParams=(1 / 3, 1, vel_disp),
correct=True,
niter=20)
edf = evolveddiskdf(dfc, lp, to=tform)
oortA, grid, derivRGrid, derivphiGrid = edf.oortA(R=1,
phi=0,
gridpoints=101,
derivGridpoints=101,
grid=True,
derivphiGrid=True,
derivRGrid=True,
nsigma=6.,
returnGrids=True)
oortB = edf.oortB(R=1,
phi=0,
grid=grid,
derivphiGrid=derivphiGrid,
derivRGrid=derivRGrid,
