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_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_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_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_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 add_spiral(arms):
#spiral parameters and defaults then generate a value for it
sp_m = arms #4
sp_spv = (22.5, 7.5) #20 15-30
sp_spv = generate_rand(sp_spv[0], sp_spv[1])
if sp_m == 4:
sp_i = (-12 * (3.1415 / 180), 2 * (3.1415 / 180)
) #12, err = 2 6, err = 1 (2 arms)
sp_i = generate_rand(sp_i[0], sp_i[1])
if sp_m == 2:
sp_i = (-6 * (3.1415 / 180), 1 * (3.1415 / 180)
) #12, err = 2 6, err = 1 (2 arms)
sp_i = generate_rand(sp_i[0], sp_i[1])
sp_x_0 = (-120 * (3.1415 / 180), 10 * (3.1415 / 180)) #-120 err = 10
sp_x_0 = generate_rand(sp_x_0[0], sp_x_0[1])
sp_a = -sp_m / tan(sp_i)
sp_gamma = sp_x_0 / sp_m
sp_fr_0 = (0.05, 0.01)
sp_fr_0 = generate_rand(sp_fr_0[0], sp_fr_0[1])
# ro = 8
# vo = 220
sp_A = -(
1
) * sp_fr_0 #*((ro*vo)**2) #not sure on this part but adding these makes it too big so leave them
sp_component = SteadyLogSpiralPotential(amp=1,
omegas=sp_spv / 220,
A=sp_A,
alpha=sp_a,
gamma=sp_gamma)
mwp_2D.append(sp_component)
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 add_spiral(arms):
#spiral parameters and defaults then generate a value for it
sp_m = arms #4
sp_spv = (22.5, 7.5) #20 15-30
sp_spv = generate_rand(sp_spv[0], sp_spv[1])
if sp_m == 4:
sp_i = (-12 * (3.1415 / 180), 2 * (3.1415 / 180)
) #12, err = 2 6, err = 1 (2 arms)
sp_i = generate_rand(sp_i[0], sp_i[1])
if sp_m == 2:
sp_i = (-6 * (3.1415 / 180), 1 * (3.1415 / 180)
) #12, err = 2 6, err = 1 (2 arms)
sp_i = generate_rand(sp_i[0], sp_i[1])
sp_x_0 = (-120 * (3.1415 / 180), 10 * (3.1415 / 180)) #-120 err = 10
sp_x_0 = generate_rand(sp_x_0[0], sp_x_0[1])
sp_a = -sp_m / tan(sp_i)
sp_gamma = sp_x_0 / sp_m
sp_fr_0 = (0.05, 0.01)
sp_fr_0 = generate_rand(sp_fr_0[0], sp_fr_0[1])
# ro = 8
# vo = 220
sp_A = -sp_fr_0 #to get from ramirez to galpy divide by 220 below so its in natural units
sp_component = SteadyLogSpiralPotential(amp=1,
omegas=sp_spv / 220,
A=sp_A,
alpha=sp_a,
gamma=sp_gamma)
mwp_2D.append(sp_component) # not yet
def plot_2d(sp=False):
mwp_2D = [i.toPlanar() for i in mwp]
sun_2D = sun.toPlanar()
sun_2D.integrate(ts, mwp_2D, method='odeint')
#spiral parameters and defaults
if sp == True:
sp_m = 4 #4 #2
sp_spv = 25 #20 #
sp_i = -5 * (3.1415 / 180) #-12 #-5
sp_x_0 = -120 * (3.1415 / 180) #-120 #
sp_a = -sp_m / tan(sp_i)
sp_gamma = sp_x_0 / sp_m
sp_fr_0 = 0.05 #0.05 #
ro = 8
vo = 220
sp_A = -(
sp_fr_0
) #divide by row to normalise?? #*((vo)**2) #not sure on this part but adding these makes it too big so leave them
sp_component = SteadyLogSpiralPotential(amp=1,
omegas=sp_spv / 220,
A=sp_A,
alpha=sp_a,
gamma=sp_gamma)
mwp_2D.append(sp_component) # not yet
for o in orbits:
o_2D = o.toPlanar()
o_2D.integrate(ts, mwp_2D, method='odeint')
delta_x = o_2D.x(ts) - sun_2D.x(ts)
delta_y = o_2D.y(ts) - sun_2D.y(ts)
delta = (delta_x**2 + delta_y**2)**0.5
plt.semilogy(sun_2D.time(ts), delta * 1000)
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_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
sp_a = -sp_m / tan(sp_i)
sp_gamma = sp_x_0 / sp_m
sp_fr_0 = 0.05 #0.05
ro = 8
vo = 220
sp_A = -(
1
) * sp_fr_0 #*((ro*vo)**2) #not sure on this part but adding these makes it too big so leave them
#galpy natural units takes care of this stuff - so just leave it
#there was a lot of jumbling to get it to look the same as literature
#just believe it!!!
sp_component = SteadyLogSpiralPotential(amp=1,
omegas=sp_spv,
A=sp_A,
alpha=sp_a,
gamma=sp_gamma,
ro=8,
vo=220)
mwp_2D.append(sp_component) # not yet
print("finally calculating distance from sun...")
for o in orbits_2D[2:]:
o_2D = o.toPlanar()
o_2D.integrate(ts, mwp_2D, method='odeint')
delta_x = o_2D.x(ts) - sun_2D.x(ts)
delta_y = o_2D.y(ts) - sun_2D.y(ts)
delta = (delta_x**2 + delta_y**2)**0.5
plt.semilogy(sun_2D.time(ts), delta * 1000)
plt.title("Solar Sibling Proximity")