def __init__(self,*args,**kwargs):
"""
NAME:
__init__
PURPOSE:
initialize an actionAngleStaeckel object
INPUT:
pot= potential or list of potentials (3D)
delta= focus (can be Quantity)
useu0 - use u0 to calculate dV (NOT recommended)
c= if True, always use C for calculations
order= (10) number of points to use in the Gauss-Legendre numerical integration of the relevant action, frequency, and angle integrals
ro= distance from vantage point to GC (kpc; can be Quantity)
vo= circular velocity at ro (km/s; can be Quantity)
OUTPUT:
instance
HISTORY:
2012-11-27 - Written - Bovy (IAS)
"""
actionAngle.__init__(self,
ro=kwargs.get('ro',None),vo=kwargs.get('vo',None))
if not 'pot' in kwargs: #pragma: no cover
raise IOError("Must specify pot= for actionAngleStaeckel")
self._pot= flatten_potential(kwargs['pot'])
if self._pot == MWPotential:
warnings.warn("Use of MWPotential as a Milky-Way-like potential is deprecated; galpy.potential.MWPotential2014, a potential fit to a large variety of dynamical constraints (see Bovy 2015), is the preferred Milky-Way-like potential in galpy",
galpyWarning)
if not 'delta' in kwargs: #pragma: no cover
raise IOError("Must specify delta= for actionAngleStaeckel")
if ext_loaded and (('c' in kwargs and kwargs['c'])
or not 'c' in kwargs):
self._c= _check_c(self._pot)
if 'c' in kwargs and kwargs['c'] and not self._c:
warnings.warn("C module not used because potential does not have a C implementation",galpyWarning) #pragma: no cover
else:
self._c= False
self._useu0= kwargs.get('useu0',False)
self._delta= kwargs['delta']
self._order= kwargs.get('order',10)
if _APY_LOADED and isinstance(self._delta,units.Quantity):
self._delta= self._delta.to(units.kpc).value/self._ro
# Check the units
self._check_consistent_units()
return None
def __init__(self, *args, **kwargs):
"""
NAME:
__init__
PURPOSE:
initialize an actionAngleAdiabatic object
INPUT:
pot= potential or list of potentials (planarPotentials)
gamma= (default=1.) replace Lz by Lz+gamma Jz in effective potential
ro= distance from vantage point to GC (kpc; can be Quantity)
vo= circular velocity at ro (km/s; can be Quantity)
OUTPUT:
instance
HISTORY:
2012-07-26 - Written - Bovy (IAS@MPIA)
"""
actionAngle.__init__(self,
ro=kwargs.get('ro', None),
vo=kwargs.get('vo', None))
if not 'pot' in kwargs: #pragma: no cover
raise IOError("Must specify pot= for actionAngleAxi")
self._pot = flatten_potential(kwargs['pot'])
if self._pot == MWPotential:
warnings.warn(
"Use of MWPotential as a Milky-Way-like potential is deprecated; galpy.potential.MWPotential2014, a potential fit to a large variety of dynamical constraints (see Bovy 2015), is the preferred Milky-Way-like potential in galpy",
galpyWarning)
if ext_loaded and 'c' in kwargs and kwargs['c']:
self._c = _check_c(self._pot)
if 'c' in kwargs and kwargs['c'] and not self._c:
warnings.warn(
"C module not used because potential does not have a C implementation",
galpyWarning) #pragma: no cover
else:
self._c = False
self._gamma = kwargs.get('gamma', 1.)
# Check the units
self._check_consistent_units()
return None
def __init__(self, *args, **kwargs):
"""
NAME:
__init__
PURPOSE:
initialize an actionAngleTorus object
INPUT:
pot= potential or list of potentials (3D)
tol= default tolerance to use when fitting tori (|dJ|/J)
dJ= default action difference when computing derivatives (Hessian or Jacobian)
OUTPUT:
instance
HISTORY:
2015-08-07 - Written - Bovy (UofT)
"""
if not 'pot' in kwargs: #pragma: no cover
raise IOError("Must specify pot= for actionAngleTorus")
self._pot = flatten_potential(kwargs['pot'])
if _isNonAxi(self._pot):
raise RuntimeError(
"actionAngleTorus for non-axisymmetric potentials is not supported"
)
if self._pot == MWPotential:
warnings.warn(
"Use of MWPotential as a Milky-Way-like potential is deprecated; galpy.potential.MWPotential2014, a potential fit to a large variety of dynamical constraints (see Bovy 2015), is the preferred Milky-Way-like potential in galpy",
galpyWarning)
if ext_loaded:
self._c = _check_c(self._pot)
if not self._c:
raise RuntimeError(
'The given potential is not fully implemented in C; using the actionAngleTorus code is not supported in pure Python'
)
else: # pragma: no cover
raise RuntimeError(
'actionAngleTorus instances cannot be used, because the actionAngleTorus_c extension failed to load'
)
self._tol = kwargs.get('tol', 0.001)
self._dJ = kwargs.get('dJ', 0.001)
return None
def __init__(self,*args,**kwargs):
"""
NAME:
__init__
PURPOSE:
initialize an actionAngleSpherical object
INPUT:
pot= a Spherical potential
ro= distance from vantage point to GC (kpc; can be Quantity)
vo= circular velocity at ro (km/s; can be Quantity)
OUTPUT:
instance
HISTORY:
2013-12-28 - Written - Bovy (IAS)
"""
actionAngle.__init__(self,
ro=kwargs.get('ro',None),vo=kwargs.get('vo',None))
if not 'pot' in kwargs: #pragma: no cover
raise IOError("Must specify pot= for actionAngleSpherical")
self._pot= flatten_potential(kwargs['pot'])
#Also store a 'planar' (2D) version of the potential
if isinstance(self._pot,list):
self._2dpot= [p.toPlanar() for p in self._pot]
else:
self._2dpot= self._pot.toPlanar()
#The following for if we ever implement this code in C
self._c= False
ext_loaded= False
if ext_loaded and (('c' in kwargs and kwargs['c'])
or not 'c' in kwargs):
self._c= True #pragma: no cover
else:
self._c= False
# Check the units
self._check_consistent_units()
return None
def __init__(self,*args,**kwargs):
"""
NAME:
__init__
PURPOSE:
initialize an actionAngleAdiabatic object
INPUT:
pot= potential or list of potentials (planarPotentials)
gamma= (default=1.) replace Lz by Lz+gamma Jz in effective potential
ro= distance from vantage point to GC (kpc; can be Quantity)
vo= circular velocity at ro (km/s; can be Quantity)
OUTPUT:
instance
HISTORY:
2012-07-26 - Written - Bovy (IAS@MPIA)
"""
actionAngle.__init__(self,
ro=kwargs.get('ro',None),vo=kwargs.get('vo',None))
if not 'pot' in kwargs: #pragma: no cover
raise IOError("Must specify pot= for actionAngleAxi")
self._pot= flatten_potential(kwargs['pot'])
if self._pot == MWPotential:
warnings.warn("Use of MWPotential as a Milky-Way-like potential is deprecated; galpy.potential.MWPotential2014, a potential fit to a large variety of dynamical constraints (see Bovy 2015), is the preferred Milky-Way-like potential in galpy",
galpyWarning)
if ext_loaded and 'c' in kwargs and kwargs['c']:
self._c= _check_c(self._pot)
if 'c' in kwargs and kwargs['c'] and not self._c:
warnings.warn("C module not used because potential does not have a C implementation",galpyWarning) #pragma: no cover
else:
self._c= False
self._gamma= kwargs.get('gamma',1.)
# Check the units
self._check_consistent_units()
return None
def __init__(self,*args,**kwargs):
"""
NAME:
__init__
PURPOSE:
initialize an actionAngleIsochroneApprox object
INPUT:
Either:
b= scale parameter of the isochrone parameter (can be Quantity)
ip= instance of a IsochronePotential
aAI= instance of an actionAngleIsochrone
pot= potential to calculate action-angle variables for
tintJ= (default: 100) time to integrate orbits for to estimate actions (can be Quantity)
ntintJ= (default: 10000) number of time-integration points
integrate_method= (default: 'dopr54_c') integration method to use
dt= (None) orbit.integrate dt keyword (for fixed stepsize integration)
maxn= (default: 3) Default value for all methods when using a grid in vec(n) up to this n (zero-based)
ro= distance from vantage point to GC (kpc; can be Quantity)
vo= circular velocity at ro (km/s; can be Quantity)
OUTPUT:
instance
HISTORY:
2013-09-10 - Written - Bovy (IAS)
"""
actionAngle.__init__(self,
ro=kwargs.get('ro',None),vo=kwargs.get('vo',None))
if not 'pot' in kwargs: #pragma: no cover
raise IOError("Must specify pot= for actionAngleIsochroneApprox")
self._pot= flatten_potential(kwargs['pot'])
if self._pot == MWPotential:
warnings.warn("Use of MWPotential as a Milky-Way-like potential is deprecated; galpy.potential.MWPotential2014, a potential fit to a large variety of dynamical constraints (see Bovy 2015), is the preferred Milky-Way-like potential in galpy",
galpyWarning)
if not 'b' in kwargs and not 'ip' in kwargs \
and not 'aAI' in kwargs: #pragma: no cover
raise IOError("Must specify b=, ip=, or aAI= for actionAngleIsochroneApprox")
if 'aAI' in kwargs:
if not isinstance(kwargs['aAI'],actionAngleIsochrone): #pragma: no cover
raise IOError("'Provided aAI= does not appear to be an instance of an actionAngleIsochrone")
self._aAI= kwargs['aAI']
elif 'ip' in kwargs:
ip= kwargs['ip']
if not isinstance(ip,IsochronePotential): #pragma: no cover
raise IOError("'Provided ip= does not appear to be an instance of an IsochronePotential")
self._aAI= actionAngleIsochrone(ip=ip)
else:
if _APY_LOADED and isinstance(kwargs['b'],units.Quantity):
b= kwargs['b'].to(units.kpc).value/self._ro
else:
b= kwargs['b']
self._aAI= actionAngleIsochrone(ip=IsochronePotential(b=b,
normalize=1.))
self._tintJ= kwargs.get('tintJ',100.)
if _APY_LOADED and isinstance(self._tintJ,units.Quantity):
self._tintJ= self._tintJ.to(units.Gyr).value\
/time_in_Gyr(self._vo,self._ro)
self._ntintJ= kwargs.get('ntintJ',10000)
self._integrate_dt= kwargs.get('dt',None)
self._tsJ= nu.linspace(0.,self._tintJ,self._ntintJ)
self._integrate_method= kwargs.get('integrate_method','dopr54_c')
self._maxn= kwargs.get('maxn',3)
self._c= False
ext_loaded= False
if ext_loaded and (('c' in kwargs and kwargs['c'])
or not 'c' in kwargs): #pragma: no cover
self._c= True
else:
self._c= False
# Check the units
self._check_consistent_units()
return None
def __init__(self,pot=None,zmax=1.,gamma=1.,Rmax=5.,
nR=16,nEz=16,nEr=31,nLz=31,numcores=1,
**kwargs):
"""
NAME:
__init__
PURPOSE:
initialize an actionAngleAdiabaticGrid object
INPUT:
pot= potential or list of potentials
zmax= zmax for building Ez grid
Rmax = Rmax for building grids
gamma= (default=1.) replace Lz by Lz+gamma Jz in effective potential
nEz=, nEr=, nLz, nR= grid size
numcores= number of cpus to use to parallellize
c= if True, use C to calculate actions
ro= distance from vantage point to GC (kpc; can be Quantity)
vo= circular velocity at ro (km/s; can be Quantity)
+scipy.integrate.quad keywords
OUTPUT:
instance
HISTORY:
2012-07-27 - Written - Bovy (IAS@MPIA)
"""
actionAngle.__init__(self,
ro=kwargs.get('ro',None),vo=kwargs.get('vo',None))
if pot is None: #pragma: no cover
raise IOError("Must specify pot= for actionAngleAxi")
self._c= kwargs.pop('c',False)
self._gamma= gamma
self._pot= flatten_potential(pot)
self._zmax= zmax
self._Rmax= Rmax
self._Rmin= 0.01
#Set up the actionAngleAdiabatic object that we will use to interpolate
self._aA= actionAngleAdiabatic(pot=self._pot,gamma=self._gamma,
c=self._c)
#Build grid for Ez, first calculate Ez(zmax;R) function
self._Rs= numpy.linspace(self._Rmin,self._Rmax,nR)
self._EzZmaxs= _evaluatePotentials(self._pot,self._Rs,
self._zmax*numpy.ones(nR))\
-_evaluatePotentials(self._pot,self._Rs,numpy.zeros(nR))
self._EzZmaxsInterp= interpolate.InterpolatedUnivariateSpline(self._Rs,numpy.log(self._EzZmaxs),k=3)
y= numpy.linspace(0.,1.,nEz)
jz= numpy.zeros((nR,nEz))
jzEzzmax= numpy.zeros(nR)
thisRs= (numpy.tile(self._Rs,(nEz,1)).T).flatten()
thisEzZmaxs= (numpy.tile(self._EzZmaxs,(nEz,1)).T).flatten()
thisy= (numpy.tile(y,(nR,1))).flatten()
if self._c:
jz= self._aA(thisRs,
numpy.zeros(len(thisRs)),
numpy.ones(len(thisRs)),#these two r dummies
numpy.zeros(len(thisRs)),
numpy.sqrt(2.*thisy*thisEzZmaxs),
**kwargs)[2]
jz= numpy.reshape(jz,(nR,nEz))
jzEzzmax[0:nR]= jz[:,nEz-1]
else:
if numcores > 1:
jz= multi.parallel_map((lambda x: self._aA(thisRs[x],0.,1.,#these two r dummies
0.,math.sqrt(2.*thisy[x]*thisEzZmaxs[x]),
_justjz=True,
**kwargs)[2]),
range(nR*nEz),numcores=numcores)
jz= numpy.reshape(jz,(nR,nEz))
jzEzzmax[0:nR]= jz[:,nEz-1]
else:
for ii in range(nR):
for jj in range(nEz):
#Calculate Jz
jz[ii,jj]= self._aA(self._Rs[ii],0.,1.,#these two r dummies
0.,numpy.sqrt(2.*y[jj]*self._EzZmaxs[ii]),
_justjz=True,**kwargs)[2]
if jj == nEz-1:
jzEzzmax[ii]= jz[ii,jj]
for ii in range(nR): jz[ii,:]/= jzEzzmax[ii]
#First interpolate Ez=Ezmax
self._jzEzmaxInterp= interpolate.InterpolatedUnivariateSpline(self._Rs,numpy.log(jzEzzmax+10.**-5.),k=3)
self._jz= jz
self._jzInterp= interpolate.RectBivariateSpline(self._Rs,
y,
jz,
kx=3,ky=3,s=0.)
#JR grid
self._Lzmin= 0.01
self._Lzs= numpy.linspace(self._Lzmin,
self._Rmax\
*galpy.potential.vcirc(self._pot,
self._Rmax),
nLz)
self._Lzmax= self._Lzs[-1]
#Calculate ER(vr=0,R=RL)
self._RL= numpy.array([galpy.potential.rl(self._pot,l) for l in self._Lzs])
self._RLInterp= interpolate.InterpolatedUnivariateSpline(self._Lzs,
self._RL,k=3)
self._ERRL= _evaluatePotentials(self._pot,self._RL,numpy.zeros(nLz)) +self._Lzs**2./2./self._RL**2.
self._ERRLmax= numpy.amax(self._ERRL)+1.
self._ERRLInterp= interpolate.InterpolatedUnivariateSpline(self._Lzs,
numpy.log(-(self._ERRL-self._ERRLmax)),k=3)
self._Ramax= 99.
self._ERRa= _evaluatePotentials(self._pot,self._Ramax,0.) +self._Lzs**2./2./self._Ramax**2.
self._ERRamax= numpy.amax(self._ERRa)+1.
self._ERRaInterp= interpolate.InterpolatedUnivariateSpline(self._Lzs,
numpy.log(-(self._ERRa-self._ERRamax)),k=3)
y= numpy.linspace(0.,1.,nEr)
jr= numpy.zeros((nLz,nEr))
jrERRa= numpy.zeros(nLz)
thisRL= (numpy.tile(self._RL,(nEr-1,1)).T).flatten()
thisLzs= (numpy.tile(self._Lzs,(nEr-1,1)).T).flatten()
thisERRL= (numpy.tile(self._ERRL,(nEr-1,1)).T).flatten()
thisERRa= (numpy.tile(self._ERRa,(nEr-1,1)).T).flatten()
thisy= (numpy.tile(y[0:-1],(nLz,1))).flatten()
if self._c:
mjr= self._aA(thisRL,
numpy.sqrt(2.*(thisERRa+thisy*(thisERRL-thisERRa)-_evaluatePotentials(self._pot,thisRL,numpy.zeros((nEr-1)*nLz)))-thisLzs**2./thisRL**2.),
thisLzs/thisRL,
numpy.zeros(len(thisRL)),
numpy.zeros(len(thisRL)),
**kwargs)[0]
jr[:,0:-1]= numpy.reshape(mjr,(nLz,nEr-1))
jrERRa[0:nLz]= jr[:,0]
else:
if numcores > 1:
mjr= multi.parallel_map((lambda x: self._aA(thisRL[x],
numpy.sqrt(2.*(thisERRa[x]+thisy[x]*(thisERRL[x]-thisERRa[x])-_evaluatePotentials(self._pot,thisRL[x],0.))-thisLzs[x]**2./thisRL[x]**2.),
thisLzs[x]/thisRL[x],
0.,0.,
_justjr=True,
**kwargs)[0]),
range((nEr-1)*nLz),
numcores=numcores)
jr[:,0:-1]= numpy.reshape(mjr,(nLz,nEr-1))
jrERRa[0:nLz]= jr[:,0]
else:
for ii in range(nLz):
for jj in range(nEr-1): #Last one is zero by construction
try:
jr[ii,jj]= self._aA(self._RL[ii],
numpy.sqrt(2.*(self._ERRa[ii]+y[jj]*(self._ERRL[ii]-self._ERRa[ii])-_evaluatePotentials(self._pot,self._RL[ii],0.))-self._Lzs[ii]**2./self._RL[ii]**2.),
self._Lzs[ii]/self._RL[ii],
0.,0.,
_justjr=True,
**kwargs)[0]
except UnboundError: #pragma: no cover
raise
if jj == 0:
jrERRa[ii]= jr[ii,jj]
for ii in range(nLz): jr[ii,:]/= jrERRa[ii]
#First interpolate Ez=Ezmax
self._jr= jr
self._jrERRaInterp= interpolate.InterpolatedUnivariateSpline(self._Lzs,
numpy.log(jrERRa+10.**-5.),k=3)
self._jrInterp= interpolate.RectBivariateSpline(self._Lzs,
y,
jr,
kx=3,ky=3,s=0.)
# Check the units
self._check_consistent_units()
return None
def __init__(self,pot=None,delta=None,Rmax=5.,
nE=25,npsi=25,nLz=30,numcores=1,
interpecc=False,
**kwargs):
"""
NAME:
__init__
PURPOSE:
initialize an actionAngleStaeckelGrid object
INPUT:
pot= potential or list of potentials
delta= focus of prolate confocal coordinate system (can be Quantity)
Rmax = Rmax for building grids (natural units)
nE=, npsi=, nLz= grid size
interpecc= (False) if True, also interpolate the approximate eccentricity, zmax, rperi, and rapo
numcores= number of cpus to use to parallellize
ro= distance from vantage point to GC (kpc; can be Quantity)
vo= circular velocity at ro (km/s; can be Quantity)
OUTPUT:
instance
HISTORY:
2012-11-29 - Written - Bovy (IAS)
2017-12-15 - Written - Bovy (UofT)
"""
actionAngle.__init__(self,
ro=kwargs.get('ro',None),vo=kwargs.get('vo',None))
if pot is None:
raise IOError("Must specify pot= for actionAngleStaeckelGrid")
self._pot= flatten_potential(pot)
if delta is None:
raise IOError("Must specify delta= for actionAngleStaeckelGrid")
if ext_loaded and 'c' in kwargs and kwargs['c']:
self._c= True
else:
self._c= False
self._delta= delta
if _APY_LOADED and isinstance(self._delta,units.Quantity):
self._delta= self._delta.to(units.kpc).value/self._ro
self._Rmax= Rmax
self._Rmin= 0.01
#Set up the actionAngleStaeckel object that we will use to interpolate
self._aA= actionAngleStaeckel.actionAngleStaeckel(pot=self._pot,delta=self._delta,c=self._c)
#Build grid
self._Lzmin= 0.01
self._Lzs= numpy.linspace(self._Lzmin,
self._Rmax\
*galpy.potential.vcirc(self._pot,
self._Rmax),
nLz)
self._Lzmax= self._Lzs[-1]
self._nLz= nLz
#Calculate E_c(R=RL), energy of circular orbit
self._RL= numpy.array([galpy.potential.rl(self._pot,l) for l in self._Lzs])
self._RLInterp= interpolate.InterpolatedUnivariateSpline(self._Lzs,
self._RL,k=3)
self._ERL= _evaluatePotentials(self._pot,self._RL,
numpy.zeros(self._nLz))\
+self._Lzs**2./2./self._RL**2.
self._ERLmax= numpy.amax(self._ERL)+1.
self._ERLInterp= interpolate.InterpolatedUnivariateSpline(self._Lzs,
numpy.log(-(self._ERL-self._ERLmax)),k=3)
self._Ramax= 200./8.
self._ERa= _evaluatePotentials(self._pot,self._Ramax,0.) +self._Lzs**2./2./self._Ramax**2.
#self._EEsc= numpy.array([self._ERL[ii]+galpy.potential.vesc(self._pot,self._RL[ii])**2./4. for ii in range(nLz)])
self._ERamax= numpy.amax(self._ERa)+1.
self._ERaInterp= interpolate.InterpolatedUnivariateSpline(self._Lzs,
numpy.log(-(self._ERa-self._ERamax)),k=3)
y= numpy.linspace(0.,1.,nE)
self._nE= nE
psis= numpy.linspace(0.,1.,npsi)*numpy.pi/2.
self._npsi= npsi
jr= numpy.zeros((nLz,nE,npsi))
jz= numpy.zeros((nLz,nE,npsi))
u0= numpy.zeros((nLz,nE))
jrLzE= numpy.zeros((nLz))
jzLzE= numpy.zeros((nLz))
#First calculate u0
thisLzs= (numpy.tile(self._Lzs,(nE,1)).T).flatten()
thisERL= (numpy.tile(self._ERL,(nE,1)).T).flatten()
thisERa= (numpy.tile(self._ERa,(nE,1)).T).flatten()
thisy= (numpy.tile(y,(nLz,1))).flatten()
thisE= _invEfunc(_Efunc(thisERa,thisERL)+thisy*(_Efunc(thisERL,thisERL)-_Efunc(thisERa,thisERL)),thisERL)
if isinstance(self._pot,galpy.potential.interpRZPotential) and hasattr(self._pot,'_origPot'):
u0pot= self._pot._origPot
else:
u0pot= self._pot
if self._c:
mu0= actionAngleStaeckel_c.actionAngleStaeckel_calcu0(thisE,thisLzs,
u0pot,
self._delta)[0]
else:
if numcores > 1:
mu0= multi.parallel_map((lambda x: self.calcu0(thisE[x],
thisLzs[x])),
range(nE*nLz),
numcores=numcores)
else:
mu0= list(map((lambda x: self.calcu0(thisE[x],
thisLzs[x])),
range(nE*nLz)))
u0= numpy.reshape(mu0,(nLz,nE))
thisR= self._delta*numpy.sinh(u0)
thisv= numpy.reshape(self.vatu0(thisE.flatten(),thisLzs.flatten(),
u0.flatten(),
thisR.flatten()),(nLz,nE))
self.thisv= thisv
#reshape
thisLzs= numpy.reshape(thisLzs,(nLz,nE))
thispsi= numpy.tile(psis,(nLz,nE,1)).flatten()
thisLzs= numpy.tile(thisLzs.T,(npsi,1,1)).T.flatten()
thisR= numpy.tile(thisR.T,(npsi,1,1)).T.flatten()
thisv= numpy.tile(thisv.T,(npsi,1,1)).T.flatten()
mjr, mlz, mjz= self._aA(thisR, #R
thisv*numpy.cos(thispsi), #vR
thisLzs/thisR, #vT
numpy.zeros(len(thisR)), #z
thisv*numpy.sin(thispsi), #vz
fixed_quad=True)
if interpecc:
mecc, mzmax, mrperi, mrap=\
self._aA.EccZmaxRperiRap(thisR, #R
thisv*numpy.cos(thispsi), #vR
thisLzs/thisR, #vT
numpy.zeros(len(thisR)), #z
thisv*numpy.sin(thispsi)) #vz
if isinstance(self._pot,galpy.potential.interpRZPotential) and hasattr(self._pot,'_origPot'):
#Interpolated potentials have problems with extreme orbits
indx= (mjr == 9999.99)
indx+= (mjz == 9999.99)
#Re-calculate these using the original potential, hopefully not too slow
tmpaA= actionAngleStaeckel.actionAngleStaeckel(pot=self._pot._origPot,delta=self._delta,c=self._c)
mjr[indx], dum, mjz[indx]= tmpaA(thisR[indx], #R
thisv[indx]*numpy.cos(thispsi[indx]), #vR
thisLzs[indx]/thisR[indx], #vT
numpy.zeros(numpy.sum(indx)), #z
thisv[indx]*numpy.sin(thispsi[indx]), #vz
fixed_quad=True)
if interpecc:
mecc[indx], mzmax[indx], mrperi[indx], mrap[indx]=\
self._aA.EccZmaxRperiRap(thisR[indx], #R
thisv[indx]*numpy.cos(thispsi[indx]), #vR
thisLzs[indx]/thisR[indx], #vT
numpy.zeros(numpy.sum(indx)), #z
thisv[indx]*numpy.sin(thispsi[indx])) #vz
jr= numpy.reshape(mjr,(nLz,nE,npsi))
jz= numpy.reshape(mjz,(nLz,nE,npsi))
if interpecc:
ecc= numpy.reshape(mecc,(nLz,nE,npsi))
zmax= numpy.reshape(mzmax,(nLz,nE,npsi))
rperi= numpy.reshape(mrperi,(nLz,nE,npsi))
rap= numpy.reshape(mrap,(nLz,nE,npsi))
zmaxLzE= numpy.zeros((nLz))
rperiLzE= numpy.zeros((nLz))
rapLzE= numpy.zeros((nLz))
for ii in range(nLz):
jrLzE[ii]= numpy.nanmax(jr[ii,(jr[ii,:,:] != 9999.99)])#:,:])
jzLzE[ii]= numpy.nanmax(jz[ii,(jz[ii,:,:] != 9999.99)])#:,:])
if interpecc:
zmaxLzE[ii]= numpy.amax(zmax[ii,numpy.isfinite(zmax[ii])])
rperiLzE[ii]= numpy.amax(rperi[ii,numpy.isfinite(rperi[ii])])
rapLzE[ii]= numpy.amax(rap[ii,numpy.isfinite(rap[ii])])
jrLzE[(jrLzE == 0.)]= numpy.nanmin(jrLzE[(jrLzE > 0.)])
jzLzE[(jzLzE == 0.)]= numpy.nanmin(jzLzE[(jzLzE > 0.)])
if interpecc:
zmaxLzE[(zmaxLzE == 0.)]= numpy.nanmin(zmaxLzE[(zmaxLzE > 0.)])
rperiLzE[(rperiLzE == 0.)]= numpy.nanmin(rperiLzE[(rperiLzE > 0.)])
rapLzE[(rapLzE == 0.)]= numpy.nanmin(rapLzE[(rapLzE > 0.)])
for ii in range(nLz):
jr[ii,:,:]/= jrLzE[ii]
jz[ii,:,:]/= jzLzE[ii]
if interpecc:
zmax[ii,:,:]/= zmaxLzE[ii]
rperi[ii,:,:]/= rperiLzE[ii]
rap[ii,:,:]/= rapLzE[ii]
#Deal w/ 9999.99
jr[(jr > 1.)]= 1.
jz[(jz > 1.)]= 1.
#Deal w/ NaN
jr[numpy.isnan(jr)]= 0.
jz[numpy.isnan(jz)]= 0.
if interpecc:
ecc[(ecc < 0.)]= 0.
ecc[(ecc > 1.)]= 1.
ecc[numpy.isnan(ecc)]= 0.
ecc[numpy.isinf(ecc)]= 1.
zmax[(zmax > 1.)]= 1.
zmax[numpy.isnan(zmax)]= 0.
zmax[numpy.isinf(zmax)]= 1.
rperi[(rperi > 1.)]= 1.
rperi[numpy.isnan(rperi)]= 0.
rperi[numpy.isinf(rperi)]= 0. # typically orbits that can reach 0
rap[(rap > 1.)]= 1.
rap[numpy.isnan(rap)]= 0.
rap[numpy.isinf(rap)]= 1.
#First interpolate the maxima
self._jr= jr
self._jz= jz
self._u0= u0
self._jrLzE= jrLzE
self._jzLzE= jzLzE
self._jrLzInterp= interpolate.InterpolatedUnivariateSpline(self._Lzs,
numpy.log(jrLzE+10.**-5.),k=3)
self._jzLzInterp= interpolate.InterpolatedUnivariateSpline(self._Lzs,
numpy.log(jzLzE+10.**-5.),k=3)
if interpecc:
self._ecc= ecc
self._zmax= zmax
self._rperi= rperi
self._rap= rap
self._zmaxLzE= zmaxLzE
self._rperiLzE= rperiLzE
self._rapLzE= rapLzE
self._zmaxLzInterp=\
interpolate.InterpolatedUnivariateSpline(self._Lzs,
numpy.log(zmaxLzE+10.**-5.),k=3)
self._rperiLzInterp=\
interpolate.InterpolatedUnivariateSpline(self._Lzs,
numpy.log(rperiLzE+10.**-5.),k=3)
self._rapLzInterp=\
interpolate.InterpolatedUnivariateSpline(self._Lzs,
numpy.log(rapLzE+10.**-5.),k=3)
#Interpolate u0
self._logu0Interp= interpolate.RectBivariateSpline(self._Lzs,
y,
numpy.log(u0),
kx=3,ky=3,s=0.)
#spline filter jr and jz, such that they can be used with ndimage.map_coordinates
self._jrFiltered= ndimage.spline_filter(numpy.log(self._jr+10.**-10.),order=3)
self._jzFiltered= ndimage.spline_filter(numpy.log(self._jz+10.**-10.),order=3)
if interpecc:
self._eccFiltered= ndimage.spline_filter(numpy.log(self._ecc+10.**-10.),order=3)
self._zmaxFiltered= ndimage.spline_filter(numpy.log(self._zmax+10.**-10.),order=3)
self._rperiFiltered= ndimage.spline_filter(numpy.log(self._rperi+10.**-10.),order=3)
self._rapFiltered= ndimage.spline_filter(numpy.log(self._rap+10.**-10.),order=3)
# Check the units
self._check_consistent_units()
return None
def __init__(self,
progenitor_mass,
progenitor=None,
pot=None,
tdisrupt=None,
leading=True,
meankvec=[2., 0., 0.3, 0., 0., 0.],
sigkvec=[0.4, 0., 0.4, 0.5, 0.5, 0.],
ro=None,
vo=None):
"""
NAME:
__init__
PURPOSE:
Initialize a stream spray DF model of a tidal stream
INPUT:
progenitor_mass - mass of the progenitor (can be Quantity)
tdisrupt= (5 Gyr) time since start of disruption (can be Quantity)
leading= (True) if True, model the leading part of the stream
if False, model the trailing part
progenitor= progenitor orbit as Orbit instance (will be re-integrated, so don't bother integrating the orbit before)
meankvec= (Fardal+2015-ish defaults)
sigkvec= (Fardal+2015-ish defaults)
OUTPUT:
Instance
HISTORY:
2018-07-31 - Written - Bovy (UofT)
"""
if _APY_LOADED and isinstance(progenitor_mass, units.Quantity):
progenitor_mass= progenitor_mass.to(units.Msun).value\
/bovy_conversion.mass_in_msol(self._vo,self._ro)
self._progenitor_mass = progenitor_mass
if tdisrupt is None:
self._tdisrupt = 5. / bovy_conversion.time_in_Gyr(
self._vo, self._ro)
else:
if _APY_LOADED and isinstance(tdisrupt, units.Quantity):
tdisrupt= tdisrupt.to(units.Gyr).value\
/bovy_conversion.time_in_Gyr(self._vo,self._ro)
self._tdisrupt = tdisrupt
if pot is None: #pragma: no cover
raise IOError("pot= must be set")
self._pot = flatten_potential(pot)
self._progenitor = progenitor()
self._progenitor_times = numpy.linspace(0., -self._tdisrupt, 10001)
self._progenitor.integrate(self._progenitor_times, self._pot)
self._meankvec = numpy.array(meankvec)
self._sigkvec = numpy.array(sigkvec)
if leading: self._meankvec *= -1.
return None
def __init__(self, *args, **kwargs):
"""
NAME:
__init__
PURPOSE:
initialize an actionAngleIsochroneApprox object
INPUT:
Either:
b= scale parameter of the isochrone parameter (can be Quantity)
ip= instance of a IsochronePotential
aAI= instance of an actionAngleIsochrone
pot= potential to calculate action-angle variables for
tintJ= (default: 100) time to integrate orbits for to estimate actions (can be Quantity)
ntintJ= (default: 10000) number of time-integration points
integrate_method= (default: 'dopr54_c') integration method to use
dt= (None) orbit.integrate dt keyword (for fixed stepsize integration)
maxn= (default: 3) Default value for all methods when using a grid in vec(n) up to this n (zero-based)
ro= distance from vantage point to GC (kpc; can be Quantity)
vo= circular velocity at ro (km/s; can be Quantity)
OUTPUT:
instance
HISTORY:
2013-09-10 - Written - Bovy (IAS)
"""
actionAngle.__init__(self,
ro=kwargs.get('ro', None),
vo=kwargs.get('vo', None))
if not 'pot' in kwargs: #pragma: no cover
raise IOError("Must specify pot= for actionAngleIsochroneApprox")
self._pot = flatten_potential(kwargs['pot'])
if self._pot == MWPotential:
warnings.warn(
"Use of MWPotential as a Milky-Way-like potential is deprecated; galpy.potential.MWPotential2014, a potential fit to a large variety of dynamical constraints (see Bovy 2015), is the preferred Milky-Way-like potential in galpy",
galpyWarning)
if not 'b' in kwargs and not 'ip' in kwargs \
and not 'aAI' in kwargs: #pragma: no cover
raise IOError(
"Must specify b=, ip=, or aAI= for actionAngleIsochroneApprox")
if 'aAI' in kwargs:
if not isinstance(kwargs['aAI'],
actionAngleIsochrone): #pragma: no cover
raise IOError(
"'Provided aAI= does not appear to be an instance of an actionAngleIsochrone"
)
self._aAI = kwargs['aAI']
elif 'ip' in kwargs:
ip = kwargs['ip']
if not isinstance(ip, IsochronePotential): #pragma: no cover
raise IOError(
"'Provided ip= does not appear to be an instance of an IsochronePotential"
)
self._aAI = actionAngleIsochrone(ip=ip)
else:
if _APY_LOADED and isinstance(kwargs['b'], units.Quantity):
b = kwargs['b'].to(units.kpc).value / self._ro
else:
b = kwargs['b']
self._aAI = actionAngleIsochrone(
ip=IsochronePotential(b=b, normalize=1.))
self._tintJ = kwargs.get('tintJ', 100.)
if _APY_LOADED and isinstance(self._tintJ, units.Quantity):
self._tintJ= self._tintJ.to(units.Gyr).value\
/time_in_Gyr(self._vo,self._ro)
self._ntintJ = kwargs.get('ntintJ', 10000)
self._integrate_dt = kwargs.get('dt', None)
self._tsJ = nu.linspace(0., self._tintJ, self._ntintJ)
self._integrate_method = kwargs.get('integrate_method', 'dopr54_c')
self._maxn = kwargs.get('maxn', 3)
self._c = False
ext_loaded = False
if ext_loaded and (('c' in kwargs and kwargs['c'])
or not 'c' in kwargs): #pragma: no cover
self._c = True
else:
self._c = False
# Check the units
self._check_consistent_units()
return None
def __init__(self,
pot=None,
zmax=1.,
gamma=1.,
Rmax=5.,
nR=16,
nEz=16,
nEr=31,
nLz=31,
numcores=1,
**kwargs):
"""
NAME:
__init__
PURPOSE:
initialize an actionAngleAdiabaticGrid object
INPUT:
pot= potential or list of potentials
zmax= zmax for building Ez grid
Rmax = Rmax for building grids
gamma= (default=1.) replace Lz by Lz+gamma Jz in effective potential
nEz=, nEr=, nLz, nR= grid size
numcores= number of cpus to use to parallellize
c= if True, use C to calculate actions
ro= distance from vantage point to GC (kpc; can be Quantity)
vo= circular velocity at ro (km/s; can be Quantity)
+scipy.integrate.quad keywords
OUTPUT:
instance
HISTORY:
2012-07-27 - Written - Bovy (IAS@MPIA)
"""
actionAngle.__init__(self,
ro=kwargs.get('ro', None),
vo=kwargs.get('vo', None))
if pot is None: #pragma: no cover
raise IOError("Must specify pot= for actionAngleAxi")
self._c = kwargs.pop('c', False)
self._gamma = gamma
self._pot = flatten_potential(pot)
self._zmax = zmax
self._Rmax = Rmax
self._Rmin = 0.01
#Set up the actionAngleAdiabatic object that we will use to interpolate
self._aA = actionAngleAdiabatic(pot=self._pot,
gamma=self._gamma,
c=self._c)
#Build grid for Ez, first calculate Ez(zmax;R) function
self._Rs = numpy.linspace(self._Rmin, self._Rmax, nR)
self._EzZmaxs= _evaluatePotentials(self._pot,self._Rs,
self._zmax*numpy.ones(nR))\
-_evaluatePotentials(self._pot,self._Rs,numpy.zeros(nR))
self._EzZmaxsInterp = interpolate.InterpolatedUnivariateSpline(
self._Rs, numpy.log(self._EzZmaxs), k=3)
y = numpy.linspace(0., 1., nEz)
jz = numpy.zeros((nR, nEz))
jzEzzmax = numpy.zeros(nR)
thisRs = (numpy.tile(self._Rs, (nEz, 1)).T).flatten()
thisEzZmaxs = (numpy.tile(self._EzZmaxs, (nEz, 1)).T).flatten()
thisy = (numpy.tile(y, (nR, 1))).flatten()
if self._c:
jz = self._aA(
thisRs,
numpy.zeros(len(thisRs)),
numpy.ones(len(thisRs)), #these two r dummies
numpy.zeros(len(thisRs)),
numpy.sqrt(2. * thisy * thisEzZmaxs),
**kwargs)[2]
jz = numpy.reshape(jz, (nR, nEz))
jzEzzmax[0:nR] = jz[:, nEz - 1]
else:
if numcores > 1:
jz = multi.parallel_map(
(
lambda x: self._aA(
thisRs[x],
0.,
1., #these two r dummies
0.,
math.sqrt(2. * thisy[x] * thisEzZmaxs[x]),
_justjz=True,
**kwargs)[2]),
range(nR * nEz),
numcores=numcores)
jz = numpy.reshape(jz, (nR, nEz))
jzEzzmax[0:nR] = jz[:, nEz - 1]
else:
for ii in range(nR):
for jj in range(nEz):
#Calculate Jz
jz[ii, jj] = self._aA(
self._Rs[ii],
0.,
1., #these two r dummies
0.,
numpy.sqrt(2. * y[jj] * self._EzZmaxs[ii]),
_justjz=True,
**kwargs)[2]
if jj == nEz - 1:
jzEzzmax[ii] = jz[ii, jj]
for ii in range(nR):
jz[ii, :] /= jzEzzmax[ii]
#First interpolate Ez=Ezmax
self._jzEzmaxInterp = interpolate.InterpolatedUnivariateSpline(
self._Rs, numpy.log(jzEzzmax + 10.**-5.), k=3)
self._jz = jz
self._jzInterp = interpolate.RectBivariateSpline(self._Rs,
y,
jz,
kx=3,
ky=3,
s=0.)
#JR grid
self._Lzmin = 0.01
self._Lzs= numpy.linspace(self._Lzmin,
self._Rmax\
*galpy.potential.vcirc(self._pot,
self._Rmax),
nLz)
self._Lzmax = self._Lzs[-1]
#Calculate ER(vr=0,R=RL)
self._RL = numpy.array(
[galpy.potential.rl(self._pot, l) for l in self._Lzs])
self._RLInterp = interpolate.InterpolatedUnivariateSpline(self._Lzs,
self._RL,
k=3)
self._ERRL = _evaluatePotentials(
self._pot, self._RL,
numpy.zeros(nLz)) + self._Lzs**2. / 2. / self._RL**2.
self._ERRLmax = numpy.amax(self._ERRL) + 1.
self._ERRLInterp = interpolate.InterpolatedUnivariateSpline(
self._Lzs, numpy.log(-(self._ERRL - self._ERRLmax)), k=3)
self._Ramax = 99.
self._ERRa = _evaluatePotentials(
self._pot, self._Ramax, 0.) + self._Lzs**2. / 2. / self._Ramax**2.
self._ERRamax = numpy.amax(self._ERRa) + 1.
self._ERRaInterp = interpolate.InterpolatedUnivariateSpline(
self._Lzs, numpy.log(-(self._ERRa - self._ERRamax)), k=3)
y = numpy.linspace(0., 1., nEr)
jr = numpy.zeros((nLz, nEr))
jrERRa = numpy.zeros(nLz)
thisRL = (numpy.tile(self._RL, (nEr - 1, 1)).T).flatten()
thisLzs = (numpy.tile(self._Lzs, (nEr - 1, 1)).T).flatten()
thisERRL = (numpy.tile(self._ERRL, (nEr - 1, 1)).T).flatten()
thisERRa = (numpy.tile(self._ERRa, (nEr - 1, 1)).T).flatten()
thisy = (numpy.tile(y[0:-1], (nLz, 1))).flatten()
if self._c:
mjr = self._aA(
thisRL,
numpy.sqrt(2. *
(thisERRa + thisy *
(thisERRL - thisERRa) - _evaluatePotentials(
self._pot, thisRL, numpy.zeros(
(nEr - 1) * nLz))) -
thisLzs**2. / thisRL**2.), thisLzs / thisRL,
numpy.zeros(len(thisRL)), numpy.zeros(len(thisRL)),
**kwargs)[0]
jr[:, 0:-1] = numpy.reshape(mjr, (nLz, nEr - 1))
jrERRa[0:nLz] = jr[:, 0]
else:
if numcores > 1:
mjr = multi.parallel_map((lambda x: self._aA(
thisRL[x],
numpy.sqrt(2. *
(thisERRa[x] + thisy[x] *
(thisERRL[x] - thisERRa[x]) -
_evaluatePotentials(self._pot, thisRL[
x], 0.)) - thisLzs[x]**2. / thisRL[x]**2.),
thisLzs[x] / thisRL[x],
0.,
0.,
_justjr=True,
**kwargs)[0]),
range((nEr - 1) * nLz),
numcores=numcores)
jr[:, 0:-1] = numpy.reshape(mjr, (nLz, nEr - 1))
jrERRa[0:nLz] = jr[:, 0]
else:
for ii in range(nLz):
for jj in range(nEr -
1): #Last one is zero by construction
try:
jr[ii, jj] = self._aA(
self._RL[ii],
numpy.sqrt(2. *
(self._ERRa[ii] + y[jj] *
(self._ERRL[ii] - self._ERRa[ii]) -
_evaluatePotentials(
self._pot, self._RL[ii], 0.)) -
self._Lzs[ii]**2. /
self._RL[ii]**2.),
self._Lzs[ii] / self._RL[ii],
0.,
0.,
_justjr=True,
**kwargs)[0]
except UnboundError: #pragma: no cover
raise
if jj == 0:
jrERRa[ii] = jr[ii, jj]
for ii in range(nLz):
jr[ii, :] /= jrERRa[ii]
#First interpolate Ez=Ezmax
self._jr = jr
self._jrERRaInterp = interpolate.InterpolatedUnivariateSpline(
self._Lzs, numpy.log(jrERRa + 10.**-5.), k=3)
self._jrInterp = interpolate.RectBivariateSpline(self._Lzs,
y,
jr,
kx=3,
ky=3,
s=0.)
# Check the units
self._check_consistent_units()
return None