def rg(self, lz):
"""
NAME:
rg
PURPOSE:
calculate the radius of a circular orbit of Lz
INPUT:
lz - Angular momentum
OUTPUT:
radius
HISTORY:
2012-07-25 - Written - Bovy (IAS@MPIA)
NOTE:
seems to take about ~0.5 ms for a Miyamoto-Nagai potential;
~0.75 ms for a MWPotential
about the same with or without interpolation of the rotation curve
Not sure what to do about negative lz...
"""
if isinstance(lz, numpy.ndarray):
indx = (lz > self._precomputergLzmax) * (lz <
self._precomputergLzmin)
indxc = True - indx
out = numpy.empty(lz.shape)
out[indxc] = self._rgInterp(lz[indxc])
out[indx] = numpy.array([
potential.rl(self._pot, lz[indx][ii])
for ii in range(numpy.sum(indx))
])
return out
else:
if lz > self._precomputergLzmax or lz < self._precomputergLzmin:
return potential.rl(self._pot, lz)
return self._rgInterp(lz)
def orbit_at_E_L(logE, logL, x=4. / 5., pot=MWPotential2014):
'''
return an orbit instance at a given energy and angular momentum in a given
potential
'''
Einf = evalPot(pot, 10.**5., 0.)
Rc = rl(pot, 10**logL)
Ec = evalPot(pot, Rc, 0.) + 0.5 * (10**logL)**2. / Rc**2.
Es = Ec + (Einf - Ec) * 10.**logE
Er = 2. * (Es - Ec)
vR = numpy.sqrt(x * Er)
vz = numpy.sqrt((1 - x) * Er)
o = Orbit([Rc, vR, (10**logL) / Rc, 0., vz, 0.])
return o
def test_actionAngleTorus_basic_freqs():
from galpy.actionAngle import actionAngleTorus
from galpy.potential import epifreq, omegac, verticalfreq, rl, \
JaffePotential, PowerSphericalPotential, HernquistPotential
tol= -3.
jr= 10.**-6.
jz= 10.**-6.
jp= JaffePotential(normalize=1.)
aAT= actionAngleTorus(pot=jp)
# at Lz=1
jphi= 1.
om= aAT.Freqs(jr,jphi,jz)
assert numpy.fabs((om[0]-epifreq(jp,rl(jp,jphi)))/om[0]) < 10.**tol, \
'Close-to-circular orbit does not have Or=kappa for actionAngleTorus'
assert numpy.fabs((om[1]-omegac(jp,rl(jp,jphi)))/om[1]) < 10.**tol, \
'Close-to-circular orbit does not have Ophi=omega for actionAngleTorus'
assert numpy.fabs((om[2]-verticalfreq(jp,rl(jp,jphi)))/om[2]) < 10.**tol, \
'Close-to-circular orbit does not have Oz=nu for actionAngleTorus'
# at Lz=1.5, w/ different potential
pp= PowerSphericalPotential(normalize=1.)
aAT= actionAngleTorus(pot=pp)
jphi= 1.5
om= aAT.Freqs(jr,jphi,jz)
assert numpy.fabs((om[0]-epifreq(pp,rl(pp,jphi)))/om[0]) < 10.**tol, \
'Close-to-circular orbit does not have Or=kappa for actionAngleTorus'
assert numpy.fabs((om[1]-omegac(pp,rl(pp,jphi)))/om[1]) < 10.**tol, \
'Close-to-circular orbit does not have Ophi=omega for actionAngleTorus'
assert numpy.fabs((om[2]-verticalfreq(pp,rl(pp,jphi)))/om[2]) < 10.**tol, \
'Close-to-circular orbit does not have Oz=nu for actionAngleTorus'
# at Lz=0.5, w/ different potential
tol= -2.5 # appears more difficult
hp= HernquistPotential(normalize=1.)
aAT= actionAngleTorus(pot=hp)
jphi= 0.5
om= aAT.Freqs(jr,jphi,jz)
assert numpy.fabs((om[0]-epifreq(hp,rl(hp,jphi)))/om[0]) < 10.**tol, \
'Close-to-circular orbit does not have Or=kappa for actionAngleTorus'
assert numpy.fabs((om[1]-omegac(hp,rl(hp,jphi)))/om[1]) < 10.**tol, \
'Close-to-circular orbit does not have Ophi=omega for actionAngleTorus'
assert numpy.fabs((om[2]-verticalfreq(hp,rl(hp,jphi)))/om[2]) < 10.**tol, \
'Close-to-circular orbit does not have Oz=nu for actionAngleTorus'
return None
def test_actionAngleTorus_basic():
from galpy.actionAngle import actionAngleTorus
from galpy.potential import MWPotential, rl, vcirc, \
FlattenedPowerPotential, PlummerPotential
tol= -4.
jr= 10.**-10.
jz= 10.**-10.
aAT= actionAngleTorus(pot=MWPotential)
# at R=1, Lz=1
jphi= 1.
angler= numpy.linspace(0.,2.*numpy.pi,101)
anglephi= numpy.linspace(0.,2.*numpy.pi,101)+1.
anglez= numpy.linspace(0.,2.*numpy.pi,101)+2.
RvR= aAT(jr,jphi,jz,angler,anglephi,anglez).T
assert numpy.all(numpy.fabs(RvR[0]-rl(MWPotential,jphi)) < 10.**tol), \
'circular orbit does not have constant radius for actionAngleTorus'
assert numpy.all(numpy.fabs(RvR[1]) < 10.**tol), \
'circular orbit does not have zero radial velocity for actionAngleTorus'
assert numpy.all(numpy.fabs(RvR[2]-vcirc(MWPotential,rl(MWPotential,jphi))) < 10.**tol), \
'circular orbit does not have constant vT=vc for actionAngleTorus'
assert numpy.all(numpy.fabs(RvR[3]) < 10.**tol), \
'circular orbit does not have zero vertical height for actionAngleTorus'
assert numpy.all(numpy.fabs(RvR[4]) < 10.**tol), \
'circular orbit does not have zero vertical velocity for actionAngleTorus'
# at Lz=1.5, using Plummer
tol= -3.25
pp= PlummerPotential(normalize=1.)
aAT= actionAngleTorus(pot=pp)
jphi= 1.5
RvR= aAT(jr,jphi,jz,angler,anglephi,anglez).T
assert numpy.all(numpy.fabs(RvR[0]-rl(pp,jphi)) < 10.**tol), \
'circular orbit does not have constant radius for actionAngleTorus'
assert numpy.all(numpy.fabs(RvR[1]) < 10.**tol), \
'circular orbit does not have zero radial velocity for actionAngleTorus'
assert numpy.all(numpy.fabs(RvR[2]-vcirc(pp,rl(pp,jphi))) < 10.**tol), \
'circular orbit does not have constant vT=vc for actionAngleTorus'
assert numpy.all(numpy.fabs(RvR[3]) < 10.**tol), \
'circular orbit does not have zero vertical height for actionAngleTorus'
assert numpy.all(numpy.fabs(RvR[4]) < 10.**tol), \
'circular orbit does not have zero vertical velocity for actionAngleTorus'
# at Lz=0.5, using FlattenedPowerPotential
tol= -4.
fp= FlattenedPowerPotential(normalize=1.)
aAT= actionAngleTorus(pot=fp)
jphi= 0.5
RvR= aAT(jr,jphi,jz,angler,anglephi,anglez).T
assert numpy.all(numpy.fabs(RvR[0]-rl(fp,jphi)) < 10.**tol), \
'circular orbit does not have constant radius for actionAngleTorus'
assert numpy.all(numpy.fabs(RvR[1]) < 10.**tol), \
'circular orbit does not have zero radial velocity for actionAngleTorus'
assert numpy.all(numpy.fabs(RvR[2]-vcirc(fp,rl(fp,jphi))) < 10.**tol), \
'circular orbit does not have constant vT=vc for actionAngleTorus'
assert numpy.all(numpy.fabs(RvR[3]) < 10.**tol), \
'circular orbit does not have zero vertical height for actionAngleTorus'
assert numpy.all(numpy.fabs(RvR[4]) < 10.**tol), \
'circular orbit does not have zero vertical velocity for actionAngleTorus'
return None
def test_actionAngleTorus_basic_freqs():
from galpy.actionAngle import actionAngleTorus
from galpy.potential import epifreq, omegac, verticalfreq, rl, \
JaffePotential, PowerSphericalPotential, HernquistPotential
tol= -3.
jr= 10.**-6.
jz= 10.**-6.
jp= JaffePotential(normalize=1.)
aAT= actionAngleTorus(pot=jp)
# at Lz=1
jphi= 1.
om= aAT.Freqs(jr,jphi,jz)
assert numpy.fabs((om[0]-epifreq(jp,rl(jp,jphi)))/om[0]) < 10.**tol, \
'Close-to-circular orbit does not have Or=kappa for actionAngleTorus'
assert numpy.fabs((om[1]-omegac(jp,rl(jp,jphi)))/om[1]) < 10.**tol, \
'Close-to-circular orbit does not have Ophi=omega for actionAngleTorus'
assert numpy.fabs((om[2]-verticalfreq(jp,rl(jp,jphi)))/om[2]) < 10.**tol, \
'Close-to-circular orbit does not have Oz=nu for actionAngleTorus'
# at Lz=1.5, w/ different potential
pp= PowerSphericalPotential(normalize=1.)
aAT= actionAngleTorus(pot=pp)
jphi= 1.5
om= aAT.Freqs(jr,jphi,jz)
assert numpy.fabs((om[0]-epifreq(pp,rl(pp,jphi)))/om[0]) < 10.**tol, \
'Close-to-circular orbit does not have Or=kappa for actionAngleTorus'
assert numpy.fabs((om[1]-omegac(pp,rl(pp,jphi)))/om[1]) < 10.**tol, \
'Close-to-circular orbit does not have Ophi=omega for actionAngleTorus'
assert numpy.fabs((om[2]-verticalfreq(pp,rl(pp,jphi)))/om[2]) < 10.**tol, \
'Close-to-circular orbit does not have Oz=nu for actionAngleTorus'
# at Lz=0.5, w/ different potential
tol= -2.5 # appears more difficult
hp= HernquistPotential(normalize=1.)
aAT= actionAngleTorus(pot=hp)
jphi= 0.5
om= aAT.Freqs(jr,jphi,jz)
assert numpy.fabs((om[0]-epifreq(hp,rl(hp,jphi)))/om[0]) < 10.**tol, \
'Close-to-circular orbit does not have Or=kappa for actionAngleTorus'
assert numpy.fabs((om[1]-omegac(hp,rl(hp,jphi)))/om[1]) < 10.**tol, \
'Close-to-circular orbit does not have Ophi=omega for actionAngleTorus'
assert numpy.fabs((om[2]-verticalfreq(hp,rl(hp,jphi)))/om[2]) < 10.**tol, \
'Close-to-circular orbit does not have Oz=nu for actionAngleTorus'
return None
def test_actionAngleTorus_basic():
from galpy.actionAngle import actionAngleTorus
from galpy.potential import MWPotential, rl, vcirc, \
FlattenedPowerPotential, PlummerPotential
tol= -4.
jr= 10.**-10.
jz= 10.**-10.
aAT= actionAngleTorus(pot=MWPotential)
# at R=1, Lz=1
jphi= 1.
angler= numpy.linspace(0.,2.*numpy.pi,101)
anglephi= numpy.linspace(0.,2.*numpy.pi,101)+1.
anglez= numpy.linspace(0.,2.*numpy.pi,101)+2.
RvR= aAT(jr,jphi,jz,angler,anglephi,anglez).T
assert numpy.all(numpy.fabs(RvR[0]-rl(MWPotential,jphi)) < 10.**tol), \
'circular orbit does not have constant radius for actionAngleTorus'
assert numpy.all(numpy.fabs(RvR[1]) < 10.**tol), \
'circular orbit does not have zero radial velocity for actionAngleTorus'
assert numpy.all(numpy.fabs(RvR[2]-vcirc(MWPotential,rl(MWPotential,jphi))) < 10.**tol), \
'circular orbit does not have constant vT=vc for actionAngleTorus'
assert numpy.all(numpy.fabs(RvR[3]) < 10.**tol), \
'circular orbit does not have zero vertical height for actionAngleTorus'
assert numpy.all(numpy.fabs(RvR[4]) < 10.**tol), \
'circular orbit does not have zero vertical velocity for actionAngleTorus'
# at Lz=1.5, using Plummer
tol= -3.25
pp= PlummerPotential(normalize=1.)
aAT= actionAngleTorus(pot=pp)
jphi= 1.5
RvR= aAT(jr,jphi,jz,angler,anglephi,anglez).T
assert numpy.all(numpy.fabs(RvR[0]-rl(pp,jphi)) < 10.**tol), \
'circular orbit does not have constant radius for actionAngleTorus'
assert numpy.all(numpy.fabs(RvR[1]) < 10.**tol), \
'circular orbit does not have zero radial velocity for actionAngleTorus'
assert numpy.all(numpy.fabs(RvR[2]-vcirc(pp,rl(pp,jphi))) < 10.**tol), \
'circular orbit does not have constant vT=vc for actionAngleTorus'
assert numpy.all(numpy.fabs(RvR[3]) < 10.**tol), \
'circular orbit does not have zero vertical height for actionAngleTorus'
assert numpy.all(numpy.fabs(RvR[4]) < 10.**tol), \
'circular orbit does not have zero vertical velocity for actionAngleTorus'
# at Lz=0.5, using FlattenedPowerPotential
tol= -4.
fp= FlattenedPowerPotential(normalize=1.)
aAT= actionAngleTorus(pot=fp)
jphi= 0.5
RvR= aAT(jr,jphi,jz,angler,anglephi,anglez).T
assert numpy.all(numpy.fabs(RvR[0]-rl(fp,jphi)) < 10.**tol), \
'circular orbit does not have constant radius for actionAngleTorus'
assert numpy.all(numpy.fabs(RvR[1]) < 10.**tol), \
'circular orbit does not have zero radial velocity for actionAngleTorus'
assert numpy.all(numpy.fabs(RvR[2]-vcirc(fp,rl(fp,jphi))) < 10.**tol), \
'circular orbit does not have constant vT=vc for actionAngleTorus'
assert numpy.all(numpy.fabs(RvR[3]) < 10.**tol), \
'circular orbit does not have zero vertical height for actionAngleTorus'
assert numpy.all(numpy.fabs(RvR[4]) < 10.**tol), \
'circular orbit does not have zero vertical velocity for actionAngleTorus'
return None
def calc_delta_MWPotential2014(savefilename,plotfilename):
Lmin, Lmax= 0.01, 10.
if not os.path.exists(savefilename):
#Setup grid
nL, nE= 101,101
Ls= 10.**numpy.linspace(numpy.log10(Lmin),numpy.log10(Lmax),nL)
#Integration times
ts= numpy.linspace(0.,20.,1001)
deltas= numpy.empty((nL,nE))
Einf= evalPot(10.**12.,0.,MWPotential2014)
print Einf
for ii in range(nL):
#Calculate Ec
Rc= rl(MWPotential2014,Ls[ii])
print ii, "Rc = ", Rc*8.
Ec= evalPot(Rc,0.,MWPotential2014)+0.5*Ls[ii]**2./Rc**2.
Es= Ec+(Einf-Ec)*10.**numpy.linspace(-2.,0.,nE)
for jj in range(nE):
#Setup an orbit with this energy and angular momentum
Er= 2.*(Es[jj]-Ec) #Random energy times 2 = vR^2 + vz^2
vR= numpy.sqrt(4./5.*Er)
vz= numpy.sqrt(1./5.*Er)
o= Orbit([Rc,vR,Ls[ii]/Rc,0.,vz,0.])
o.integrate(ts,MWPotential2014,method='symplec4_c')
deltas[ii,jj]= estimateDeltaStaeckel(o.R(ts),o.z(ts),
pot=MWPotential2014)
#Save
save_pickles(savefilename,deltas)
else:
savefile= open(savefilename,'rb')
deltas= pickle.load(savefile)
savefile.close()
#Plot
print numpy.nanmax(deltas)
bovy_plot.bovy_print()
bovy_plot.bovy_dens2d(deltas.T,origin='lower',cmap='jet',
xrange=[numpy.log10(Lmin),numpy.log10(Lmax)],
yrange=[-2,0.],
xlabel=r'$\log_{10} L$',
ylabel=r'$\log_{10}\left(\frac{E-E_c(L)}{E(\infty)-E_c(L)}\right)$',
colorbar=True,shrink=0.78,
zlabel=r'$\mathrm{Approximate\ focal\ length}$',
# interpolation='nearest',
vmin=0.,vmax=0.6)
bovy_plot.bovy_end_print(plotfilename)
return None
def orbits_on_grid(logE_range=[-2., 0.],
logL_range=[-2., 1.],
nE=101,
nL=101,
x=4. / 5.,
pot=MWPotential2014):
'''
generate initial phase-space points for a grid of orbits in a given potential
'''
grid = np.empty([nE, nL, 6])
logEs = np.linspace(logE_range[0], logE_range[1], nE)
logLs = np.linspace(logL_range[0], logL_range[1], nL)
Einf = evalPot(pot, 10.**12., 0.)
for j in range(nL):
Rc = rl(pot, 10**logLs[j])
Ec = evalPot(pot, Rc, 0.) + 0.5 * (10**logLs[j])**2. / Rc**2.
Es = Ec + (Einf - Ec) * 10.**logEs
for i in range(nE):
Er = 2. * (Es[i] - Ec)
vR = numpy.sqrt(x * Er)
vz = numpy.sqrt((1 - x) * Er)
grid[i, j] = [Rc, vR, (10**logLs[j]) / Rc, 0., vz, 0.]
return grid
def __init__(self,
hr,
sr,
sz,
hsr,
hsz,
pot=None,
aA=None,
cutcounter=False,
_precomputerg=True,
_precomputergrmax=None,
_precomputergnLz=51,
ro=1.,
lo=10. / 220. * 8.):
"""
NAME:
__init__
PURPOSE:
Initialize a quasi-isothermal DF
INPUT:
hr - radial scale length
sr - radial velocity dispersion at the solar radius
sz - vertical velocity dispersion at the solar radius
hsr - radial-velocity-dispersion scale length
hsz - vertial-velocity-dispersion scale length
pot= Potential instance or list thereof
aA= actionAngle instance used to convert (x,v) to actions
cutcounter= if True, set counter-rotating stars' DF to zero
ro= reference radius for surface mass and sigmas
lo= reference angular momentum below where there are significant numbers of retrograde stars
OTHER INPUTS:
_precomputerg= if True (default), pre-compute the rL(L)
_precomputergrmax= if set, this is the maximum R for which to pre-compute rg (default: 5*hr)
_precomputergnLz if set, number of Lz to pre-compute rg for (default: 51)
OUTPUT:
object
HISTORY:
2012-07-25 - Started - Bovy (IAS@MPIA)
"""
self._hr = hr
self._sr = sr
self._sz = sz
self._hsr = hsr
self._hsz = hsz
self._ro = ro
self._lo = lo
self._lnsr = math.log(self._sr)
self._lnsz = math.log(self._sz)
if pot is None:
raise IOError("pot= must be set")
self._pot = pot
if aA is None:
raise IOError("aA= must be set")
self._aA = aA
self._cutcounter = cutcounter
if _precomputerg:
if _precomputergrmax is None:
_precomputergrmax = 5 * self._hr
self._precomputergrmax = _precomputergrmax
self._precomputergnLz = _precomputergnLz
self._precomputergLzmin = 0.01
self._precomputergLzmax= self._precomputergrmax\
*potential.vcirc(self._pot,self._precomputergrmax)
self._precomputergLzgrid = numpy.linspace(self._precomputergLzmin,
self._precomputergLzmax,
self._precomputergnLz)
self._rls = numpy.array(
[potential.rl(self._pot, l) for l in self._precomputergLzgrid])
#Spline interpolate
self._rgInterp = interpolate.InterpolatedUnivariateSpline(
self._precomputergLzgrid, self._rls, k=3)
else:
self._precomputergrmax = 0.
self._rgInterp = None
self._rls = None
self._precomputergnr = None
self._precomputergLzgrid = None
self._precomputerg = _precomputerg
return None
def process_single(i):
vxvv = [ra[i], dec[i], distance[i], pmra[i], pmdec[i], rv[i]]
if not np.all(np.isfinite(vxvv)) or not np.all(np.isfinite(covariance[i])):
return np.ones(56) * np.nan
samp = np.random.multivariate_normal(vxvv, covariance[i], size=1000)
if not (np.all(samp[:, 1] < 90.) & np.all(samp[:, 1] > -90.)):
return np.ones(56) * np.nan
os = Orbit(np.array([
samp[:, 0], samp[:, 1], samp[:, 2], samp[:, 3], samp[:, 4], samp[:, 5]
]).T,
radec=True,
ro=_R0,
vo=_v0,
zo=_z0,
solarmotion=[-11.1, 25.7, 7.25])
sXYZ = np.dstack([os.x(), os.y(), os.z()])[0] / _R0
sRpz = np.dstack([os.R() / _R0, os.phi(), os.z() / _R0])[0]
svRvTvz = np.dstack([os.vR(), os.vT(), os.vz()])[0] / _v0
deltas = estimateDeltaStaeckel(MWPotential2014,
np.median(sRpz[:, 0]),
np.median(sRpz[:, 2]),
no_median=True)
aAS = actionAngleStaeckel(pot=MWPotential2014, delta=np.mean(deltas))
e, zmax, rperi, rap = aAS.EccZmaxRperiRap(sRpz[:, 0],
svRvTvz[:, 0],
svRvTvz[:, 1],
sRpz[:, 2],
svRvTvz[:, 2],
sRpz[:, 1],
delta=deltas)
tcov = np.cov(np.dstack([e, zmax, rperi, rap])[0].T)
errs = np.sqrt(np.diag(tcov))
e_zmax_corr = tcov[0, 1] / (errs[0] * errs[1])
e_rperi_corr = tcov[0, 2] / (errs[0] * errs[2])
e_rap_corr = tcov[0, 3] / (errs[0] * errs[3])
zmax_rperi_corr = tcov[1, 2] / (errs[1] * errs[2])
zmax_rap_corr = tcov[1, 3] / (errs[1] * errs[3])
rperi_rap_corr = tcov[2, 3] / (errs[2] * errs[3])
e_err, zmax_err, rperi_err, rap_err = errs
action = np.array(
aAS.actionsFreqsAngles(sRpz[:, 0], svRvTvz[:, 0], svRvTvz[:, 1],
sRpz[:, 2], svRvTvz[:, 2], sRpz[:, 1]))
tcov_after_action = np.cov(action)
errs = np.sqrt(np.diag(tcov_after_action))
jr_lz_corr = tcov_after_action[0, 1] / (errs[0] * errs[1])
jr_jz_corr = tcov_after_action[0, 2] / (errs[0] * errs[2])
lz_jz_corr = tcov_after_action[1, 2] / (errs[1] * errs[2])
jr_err, lz_err, jz_err, or_err, op_err, oz_err, tr_err, tphi_err, tz_err = errs
Rc = np.array([rl(MWPotential2014, lz) for lz in action[1]]) * _R0
Ec = (evaluatePotentials(MWPotential2014, Rc, 0.) + 0.5 *
(action[1])**2. / Rc**2.) * _v0**2
E = os.E(pot=MWPotential2014)
# galactocentric coord and vel uncertainty
galcen_tcov = np.cov(np.dstack([sRpz[:, 0], sRpz[:, 1], sRpz[:, 2]])[0].T)
galcen_errs = np.sqrt(np.diag(galcen_tcov))
galr_err, galphi_err, galz_err = galcen_errs
galcenv_tcov = np.cov(
np.dstack([svRvTvz[:, 0], svRvTvz[:, 1], svRvTvz[:, 2]])[0].T)
galcenv_errs = np.sqrt(np.diag(galcenv_tcov))
galvr_err, galvt_err, galvz_err = galcenv_errs
galvr_galvt_corr = galcenv_tcov[0, 1] / (galcenv_errs[0] * galcenv_errs[1])
galvr_galvz_corr = galcenv_tcov[0, 2] / (galcenv_errs[0] * galcenv_errs[2])
galvt_galvz_corr = galcenv_tcov[1, 2] / (galcenv_errs[1] * galcenv_errs[2])
# galr mean to avoid issue near GC, error propagation
x_mean = np.nanmean(sXYZ[:, 0])
y_mean = np.nanmean(sXYZ[:, 1])
x_err = np.nanstd(sXYZ[:, 0])
y_err = np.nanstd(sXYZ[:, 1])
x_err_percentage = x_err / np.nanmean(sXYZ[:, 0])
y_err_percentage = y_err / np.nanmean(sXYZ[:, 1])
x2_err = (x_mean**2) * (2 * x_err_percentage)
y2_err = (y_mean**2) * (2 * y_err_percentage)
galr = np.sqrt(x_mean**2 + y_mean**2)
galr2_err = np.sqrt(x2_err**2 + y2_err**2)
galr2_err_percentage = galr2_err / (galr**2)
galr_err = galr * (0.5 * galr2_err_percentage)
# galphi mean to avoid issue near GC, error propagation
galphi = np.arctan2(y_mean, x_mean)
galphi_err_x = (
y_mean /
(x_mean**2 + y_mean**2)) * x_err # error propagation from x_mean
galphi_err_y = (
-x_mean /
(x_mean**2 + y_mean**2)) * y_err # error propagation from y_mean
galphi_err = np.sqrt(galphi_err_x**2 + galphi_err_y**2) # add them up
return np.nanmean(e), \
e_err, \
np.nanmean(zmax) * _R0, \
zmax_err * _R0, \
np.nanmean(rperi) * _R0, \
rperi_err * _R0, \
np.nanmean(rap) * _R0, \
rap_err * _R0, \
e_zmax_corr, \
e_rperi_corr, \
e_rap_corr, \
zmax_rperi_corr, \
zmax_rap_corr, \
rperi_rap_corr, \
np.nanmean(action[0]) * _R0 * _v0, \
jr_err * _R0 * _v0, \
np.nanmean(action[1]) * _R0 * _v0, \
lz_err * _R0 * _v0, \
np.nanmean(action[2]) * _R0 * _v0, \
jz_err * _R0 * _v0, \
jr_lz_corr, \
jr_jz_corr, \
lz_jz_corr, \
np.nanmean(action[3]) * _freq, or_err * _freq, \
np.nanmean(action[4]) * _freq, op_err * _freq, \
np.nanmean(action[5]) * _freq, oz_err * _freq, \
np.nanmean(action[6]), \
tr_err, \
np.nanmean(action[7]), \
tphi_err, \
np.nanmean(action[8]), \
tz_err, \
np.nanmean(Rc), \
np.nanstd(Rc), \
np.nanmean(E), \
np.nanstd(E), \
np.nanmean(E - Ec), \
np.nanstd(E - Ec), \
galr * _R0, \
galphi, \
np.nanmean(sRpz[:, 2]) * _R0, \
np.nanmean(svRvTvz[:, 0]) * _v0, \
np.nanmean(svRvTvz[0, 1]) * _v0, \
np.nanmean(svRvTvz[:, 2]) * _v0, \
galr_err * _R0, \
galphi_err, \
galz_err * _R0, \
galvr_err * _v0, \
galvt_err * _v0, \
galvz_err * _v0, \
galvr_galvt_corr, \
galvr_galvz_corr, \
galvt_galvz_corr
def process_single(i):
vxvv = [ra[i], dec[i], distance[i], pmra[i], pmdec[i], rv[i]]
if not np.all(np.isfinite(vxvv)) or not np.all(np.isfinite(covariance[i])):
return np.ones(56) * np.nan
samp = np.random.multivariate_normal(vxvv, covariance[i], size=1000)
if not (np.all(samp[:, 1] < 90.) & np.all(samp[:, 1] > -90.)):
return np.ones(56) * np.nan
sXYZ, svxyz, sRpz, svRvTvz = obs_to_galcen(samp[:, 0],
samp[:, 1],
samp[:, 2],
samp[:, 3],
samp[:, 4],
samp[:, 5],
ro=_R0,
vo=_v0,
zo=_z0)
deltas = estimateDeltaStaeckel(MWPotential2014, np.median(sRpz[:, 0]), np.median(sRpz[:, 2]), no_median=True)
aAS = actionAngleStaeckel(pot=MWPotential2014, delta=np.mean(deltas))
e, zmax, rperi, rap = aAS.EccZmaxRperiRap(sRpz[:, 0],
svRvTvz[:, 0],
svRvTvz[:, 1],
sRpz[:, 2],
svRvTvz[:, 2],
sRpz[:, 1], delta=deltas)
tcov = np.cov(np.dstack([e, zmax, rperi, rap])[0].T)
errs = np.sqrt(np.diag(tcov))
e_zmax_corr = tcov[0, 1] / (errs[0] * errs[1])
e_rperi_corr = tcov[0, 2] / (errs[0] * errs[2])
e_rap_corr = tcov[0, 3] / (errs[0] * errs[3])
zmax_rperi_corr = tcov[1, 2] / (errs[1] * errs[2])
zmax_rap_corr = tcov[1, 3] / (errs[1] * errs[3])
rperi_rap_corr = tcov[2, 3] / (errs[2] * errs[3])
e_err, zmax_err, rperi_err, rap_err = errs
action = np.array(
aAS.actionsFreqsAngles(sRpz[:, 0], svRvTvz[:, 0], svRvTvz[:, 1], sRpz[:, 2], svRvTvz[:, 2], sRpz[:, 1]))
tcov_after_action = np.cov(action)
errs = np.sqrt(np.diag(tcov_after_action))
jr_lz_corr = tcov_after_action[0, 1] / (errs[0] * errs[1])
jr_jz_corr = tcov_after_action[0, 2] / (errs[0] * errs[2])
lz_jz_corr = tcov_after_action[1, 2] / (errs[1] * errs[2])
jr_err, lz_err, jz_err, or_err, op_err, oz_err, tr_err, tphi_err, tz_err = errs
Rc = np.array([rl(MWPotential2014, lz) for lz in action[1]]) * _R0
Ec = (evaluatePotentials(MWPotential2014, Rc, 0.) + 0.5 * (action[1]) ** 2. / Rc ** 2.) * _v0 ** 2
E = (evaluatePotentials(MWPotential2014, sRpz[:, 0], sRpz[:, 2], phi=sRpz[:, 1]) + np.sum(svRvTvz ** 2 / 2.,
axis=1)) * _v0 ** 2
# galactocentric coord and vel uncertainty
galcen_tcov = np.cov(np.dstack([sRpz[:, 0], sRpz[:, 1], sRpz[:, 2]])[0].T)
galcen_errs = np.sqrt(np.diag(galcen_tcov))
galr_err, galphi_err, galz_err = galcen_errs
galcenv_tcov = np.cov(np.dstack([svRvTvz[:, 0], svRvTvz[:, 1], svRvTvz[:, 2]])[0].T)
galcenv_errs = np.sqrt(np.diag(galcenv_tcov))
galvr_err, galvt_err, galvz_err = galcenv_errs
galvr_galvt_corr = galcenv_tcov[0, 1] / (galcenv_errs[0] * galcenv_errs[1])
galvr_galvz_corr = galcenv_tcov[0, 2] / (galcenv_errs[0] * galcenv_errs[2])
galvt_galvz_corr = galcenv_tcov[1, 2] / (galcenv_errs[1] * galcenv_errs[2])
return np.nanmean(e), e_err, np.nanmean(zmax) * _R0, zmax_err * _R0, np.nanmean(
rperi) * _R0, rperi_err * _R0, np.nanmean(rap) * _R0, rap_err * _R0, \
e_zmax_corr, e_rperi_corr, e_rap_corr, zmax_rperi_corr, zmax_rap_corr, rperi_rap_corr, \
np.nanmean(action[0]) * _R0 * _v0, jr_err * _R0 * _v0, np.nanmean(
action[1]) * _R0 * _v0, lz_err * _R0 * _v0, np.nanmean(action[2]) * _R0 * _v0, jz_err * _R0 * _v0, \
jr_lz_corr, jr_jz_corr, lz_jz_corr, \
np.nanmean(action[3]) * _freq, or_err * _freq, np.nanmean(action[4]) * _freq, op_err * _freq, np.nanmean(
action[5]) * _freq, oz_err * _freq, \
np.nanmean(action[6]), tr_err, np.nanmean(action[7]), tphi_err, np.nanmean(action[8]), tz_err, \
np.nanmean(Rc), np.nanstd(Rc), np.nanmean(E), np.nanstd(E), np.nanmean(E - Ec), np.nanstd(
E - Ec), \
np.nanmean(sRpz[:, 0]) * _R0, \
np.nanmean(sRpz[:, 1]), \
np.nanmean(sRpz[:, 2]) * _R0, \
np.nanmean(svRvTvz[:, 0]) * _v0, \
np.nanmean(svRvTvz[0, 1]) * _v0, \
np.nanmean(svRvTvz[:, 2]) * _v0, \
galr_err * _R0, \
galphi_err, \
galz_err * _R0, \
galvr_err * _v0, \
galvt_err * _v0, \
galvz_err * _v0, \
galvr_galvt_corr, \
galvr_galvz_corr, \
galvt_galvz_corr