def test_estimate_hsz():
qdf= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
pot=MWPotential,aA=aAS,cutcounter=True)
assert numpy.fabs((qdf.estimate_hsz(0.9,z=0.)-1.0)/1.0) < 0.25, 'estimated vertical-dispersion scale length deviates more from input scale length than expected'
#Another one
qdf= quasiisothermaldf(1./2.,0.2,0.1,1.,2.,
pot=MWPotential,aA=aAS,cutcounter=True)
assert numpy.fabs((qdf.estimate_hsz(0.9,z=0.05)-2.0)/2.0) < 0.25, 'estimated vertical-dispersion scale length deviates more from input scale length than expected'
return None
def test_estimate_hr():
qdf= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
pot=MWPotential,aA=aAS,cutcounter=True)
assert numpy.fabs((qdf.estimate_hr(0.9,z=0.)-0.25)/0.25) < 0.1, 'estimated scale length deviates more from input scale length than expected'
#Another one
qdf= quasiisothermaldf(1./2.,0.2,0.1,1.,1.,
pot=MWPotential,aA=aAS,cutcounter=True)
assert numpy.fabs((qdf.estimate_hr(0.9,z=None)-0.5)/0.5) < 0.15, 'estimated scale length deviates more from input scale length than expected'
#Another one
qdf= quasiisothermaldf(1.,0.2,0.1,1.,1.,
pot=MWPotential,aA=aAS,cutcounter=True)
assert numpy.fabs((qdf.estimate_hr(0.9,z=None,fixed_quad=False)-1.0)/1.0) < 0.3, 'estimated scale length deviates more from input scale length than expected'
return None
def test_call_diffinoutputs():
qdf= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
pot=MWPotential,aA=aAS,cutcounter=True)
#when specifying rg etc., first get these from a previous output
val, trg, tkappa, tnu, tOmega= qdf((0.03,0.9,0.02),_return_freqs=True)
#First check that just supplying these again works
assert numpy.fabs(val-qdf((0.03,0.9,0.02),rg=trg,kappa=tkappa,nu=tnu,
Omega=tOmega)) < 10.**-8., 'qdf calls w/ rg, and frequencies specified and w/ not specified do not agrees'
#Also calculate the frequencies
assert numpy.fabs(val-qdf((0.03,0.9,0.02),rg=trg,
kappa=epifreq(MWPotential,trg),
nu=verticalfreq(MWPotential,trg),
Omega=omegac(MWPotential,trg))) < 10.**-8., 'qdf calls w/ rg, and frequencies specified and w/ not specified do not agrees'
#Also test _return_actions
val, jr,lz,jz= qdf(0.9,0.1,0.95,0.1,0.08,_return_actions=True)
assert numpy.fabs(val-qdf((jr,lz,jz))) < 10.**-8., 'qdf call w/ R,vR,... and actions specified do not agree'
acs= aAS(0.9,0.1,0.95,0.1,0.08)
assert numpy.fabs(acs[0]-jr) < 10.**-8., 'direct calculation of jr and that returned from qdf.__call__ does not agree'
assert numpy.fabs(acs[1]-lz) < 10.**-8., 'direct calculation of lz and that returned from qdf.__call__ does not agree'
assert numpy.fabs(acs[2]-jz) < 10.**-8., 'direct calculation of jz and that returned from qdf.__call__ does not agree'
#Test unbound orbits
#Find unbound orbit, new qdf s.t. we can get UnboundError (only with
taAS= actionAngleStaeckel(pot=MWPotential,c=False,delta=0.5)
qdfnc= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
pot=MWPotential,
aA=taAS,
cutcounter=True)
from galpy.actionAngle import UnboundError
try: acs= taAS(0.9,10.,-20.,0.1,10.)
except UnboundError: pass
else:
print(acs)
raise AssertionError('Test orbit in qdf that is supposed to be unbound is not')
assert qdfnc(0.9,10.,-20.,0.1,10.) < 10.**-10., 'unbound orbit does not return qdf equal to zero'
#Test negative lz
assert qdf((0.03,-0.1,0.02)) < 10.**-8., 'qdf w/ cutcounter=True and negative lz does not return 0'
assert qdf((0.03,-0.1,0.02),log=True) <= numpy.finfo(numpy.dtype(numpy.float64)).min+1., 'qdf w/ cutcounter=True and negative lz does not return 0'
#Test func
val= qdf((0.03,0.9,0.02))
fval= qdf((0.03,0.9,0.02),func=lambda x,y,z: numpy.sin(x)*numpy.cos(y)\
*numpy.exp(z))
assert numpy.fabs(val*numpy.sin(0.03)*numpy.cos(0.9)*numpy.exp(0.02)-
fval) < 10.**-8, 'qdf __call__ w/ func does not work as expected'
lfval= qdf((0.03,0.9,0.02),func=lambda x,y,z: numpy.sin(x)*numpy.cos(y)\
*numpy.exp(z),log=True)
assert numpy.fabs(numpy.log(val)+numpy.log(numpy.sin(0.03)\
*numpy.cos(0.9)\
*numpy.exp(0.02))-
lfval) < 10.**-8, 'qdf __call__ w/ func does not work as expected'
return None
def test_pvz_staeckel_arrayin():
qdf= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
pot=MWPotential,aA=aAS,cutcounter=True)
R,z= 0.8, 0.1
pvz= qdf.pvz(0.05,R*numpy.ones(2),z*numpy.ones(2))
assert numpy.all(numpy.log(pvz)-numpy.log(qdf.pvz(0.05,R,z))) < 10.**-10., 'pvz calculated with R and z array input does not equal to calculated with scalar input'
return None
def test_qdf():
from galpy.df import quasiisothermaldf
from galpy.potential import MWPotential2014
from galpy.actionAngle import actionAngleStaeckel
# Setup actionAngle instance for action calcs
aAS= actionAngleStaeckel(pot=MWPotential2014,delta=0.45,
c=True)
# Quasi-iso df w/ hr=1/3, hsr/z=1, sr(1)=0.2, sz(1)=0.1
df= quasiisothermaldf(1./3.,0.2,0.1,1.,1.,aA=aAS,
pot=MWPotential2014)
# Evaluate DF w/ R,vR,vT,z,vz
df(0.9,0.1,0.8,0.05,0.02)
assert numpy.fabs(df(0.9,0.1,0.8,0.05,0.02)-numpy.array([ 123.57158928])) < 10.**-4., 'qdf does not behave as expected'
# Evaluate DF w/ Orbit instance, return ln
from galpy.orbit import Orbit
df(Orbit([0.9,0.1,0.8,0.05,0.02]),log=True)
assert numpy.fabs(df(Orbit([0.9,0.1,0.8,0.05,0.02]),log=True)-numpy.array([ 4.81682066])) < 10.**-4., 'qdf does not behave as expected'
# Evaluate DF marginalized over vz
df.pvRvT(0.1,0.9,0.9,0.05)
assert numpy.fabs(df.pvRvT(0.1,0.9,0.9,0.05)-23.273310451852243) < 10.**-4., 'qdf does not behave as expected'
# Evaluate DF marginalized over vR,vT
df.pvz(0.02,0.9,0.05)
assert numpy.fabs(df.pvz(0.02,0.9,0.05)-50.949586235238172) < 10.**-4., 'qdf does not behave as expected'
# Calculate the density
df.density(0.9,0.05)
assert numpy.fabs(df.density(0.9,0.05)-12.73725936526167) < 10.**-4., 'qdf does not behave as expected'
# Estimate the DF's actual density scale length at z=0
df.estimate_hr(0.9,0.)
assert numpy.fabs(df.estimate_hr(0.9,0.)-0.322420336223) < 10.**-2., 'qdf does not behave as expected'
# Estimate the DF's actual surface-density scale length
df.estimate_hr(0.9,None)
assert numpy.fabs(df.estimate_hr(0.9,None)-0.38059909132766462) < 10.**-4., 'qdf does not behave as expected'
# Estimate the DF's density scale height
df.estimate_hz(0.9,0.02)
assert numpy.fabs(df.estimate_hz(0.9,0.02)-0.064836202345657207) < 10.**-4., 'qdf does not behave as expected'
# Calculate the mean velocities
df.meanvR(0.9,0.05), df.meanvT(0.9,0.05),
df.meanvz(0.9,0.05)
assert numpy.fabs(df.meanvR(0.9,0.05)-3.8432265354618213e-18) < 10.**-4., 'qdf does not behave as expected'
assert numpy.fabs(df.meanvT(0.9,0.05)-0.90840425173325279) < 10.**-4., 'qdf does not behave as expected'
assert numpy.fabs(df.meanvz(0.9,0.05)+4.3579787517991084e-19) < 10.**-4., 'qdf does not behave as expected'
# Calculate the velocity dispersions
from numpy import sqrt
sqrt(df.sigmaR2(0.9,0.05)), sqrt(df.sigmaz2(0.9,0.05))
assert numpy.fabs(sqrt(df.sigmaR2(0.9,0.05))-0.22695537077102387) < 10.**-4., 'qdf does not behave as expected'
assert numpy.fabs(sqrt(df.sigmaz2(0.9,0.05))-0.094215523962105044) < 10.**-4., 'qdf does not behave as expected'
# Calculate the tilt of the velocity ellipsoid
# 2017/10-28: CHANGED bc tilt now returns angle in rad, no longer in deg
df.tilt(0.9,0.05)
assert numpy.fabs(df.tilt(0.9,0.05)-2.5166061974413765/180.*numpy.pi) < 10.**-4., 'qdf does not behave as expected'
# Calculate a higher-order moment of the velocity DF
df.vmomentdensity(0.9,0.05,6.,2.,2.,gl=True)
assert numpy.fabs(df.vmomentdensity(0.9,0.05,6.,2.,2.,gl=True)-0.0001591100892366438) < 10.**-4., 'qdf does not behave as expected'
# Sample velocities at given R,z, check mean
numpy.random.seed(1)
vs= df.sampleV(0.9,0.05,n=500); mvt= numpy.mean(vs[:,1])
assert numpy.fabs(numpy.mean(vs[:,0])) < 0.05 # vR
assert numpy.fabs(mvt-df.meanvT(0.9,0.05)) < 0.01 #vT
assert numpy.fabs(numpy.mean(vs[:,2])) < 0.05 # vz
return None
def test_sigmarz_staeckel_mc():
numpy.random.seed(1)
qdf= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
pot=MWPotential,aA=aAS,cutcounter=True)
#In the mid-plane, should be zero
assert numpy.fabs(qdf.sigmaRz(0.9,0.,mc=True)) < 0.05, "qdf's sigmaRz deviates more than expected from zero in the mid-plane for staeckel approx."
return None
def test_qdf():
from galpy.df import quasiisothermaldf
from galpy.potential import MWPotential2014
from galpy.actionAngle import actionAngleStaeckel
# Setup actionAngle instance for action calcs
aAS= actionAngleStaeckel(pot=MWPotential2014,delta=0.45,
c=True)
# Quasi-iso df w/ hr=1/3, hsr/z=1, sr(1)=0.2, sz(1)=0.1
df= quasiisothermaldf(1./3.,0.2,0.1,1.,1.,aA=aAS,
pot=MWPotential2014)
# Evaluate DF w/ R,vR,vT,z,vz
df(0.9,0.1,0.8,0.05,0.02)
assert numpy.fabs(df(0.9,0.1,0.8,0.05,0.02)-numpy.array([ 123.57158928])) < 10.**-4., 'qdf does not behave as expected'
# Evaluate DF w/ Orbit instance, return ln
from galpy.orbit import Orbit
df(Orbit([0.9,0.1,0.8,0.05,0.02]),log=True)
assert numpy.fabs(df(Orbit([0.9,0.1,0.8,0.05,0.02]),log=True)-numpy.array([ 4.81682066])) < 10.**-4., 'qdf does not behave as expected'
# Evaluate DF marginalized over vz
df.pvRvT(0.1,0.9,0.9,0.05)
assert numpy.fabs(df.pvRvT(0.1,0.9,0.9,0.05)-23.273310451852243) < 10.**-4., 'qdf does not behave as expected'
# Evaluate DF marginalized over vR,vT
df.pvz(0.02,0.9,0.05)
assert numpy.fabs(df.pvz(0.02,0.9,0.05)-50.949586235238172) < 10.**-4., 'qdf does not behave as expected'
# Calculate the density
df.density(0.9,0.05)
assert numpy.fabs(df.density(0.9,0.05)-12.73725936526167) < 10.**-4., 'qdf does not behave as expected'
# Estimate the DF's actual density scale length at z=0
df.estimate_hr(0.9,0.)
assert numpy.fabs(df.estimate_hr(0.9,0.)-0.322420336223) < 10.**-2., 'qdf does not behave as expected'
# Estimate the DF's actual surface-density scale length
df.estimate_hr(0.9,None)
assert numpy.fabs(df.estimate_hr(0.9,None)-0.38059909132766462) < 10.**-4., 'qdf does not behave as expected'
# Estimate the DF's density scale height
df.estimate_hz(0.9,0.02)
assert numpy.fabs(df.estimate_hz(0.9,0.02)-0.064836202345657207) < 10.**-4., 'qdf does not behave as expected'
# Calculate the mean velocities
df.meanvR(0.9,0.05), df.meanvT(0.9,0.05),
df.meanvz(0.9,0.05)
assert numpy.fabs(df.meanvR(0.9,0.05)-3.8432265354618213e-18) < 10.**-4., 'qdf does not behave as expected'
assert numpy.fabs(df.meanvT(0.9,0.05)-0.90840425173325279) < 10.**-4., 'qdf does not behave as expected'
assert numpy.fabs(df.meanvz(0.9,0.05)+4.3579787517991084e-19) < 10.**-4., 'qdf does not behave as expected'
# Calculate the velocity dispersions
from numpy import sqrt
sqrt(df.sigmaR2(0.9,0.05)), sqrt(df.sigmaz2(0.9,0.05))
assert numpy.fabs(sqrt(df.sigmaR2(0.9,0.05))-0.22695537077102387) < 10.**-4., 'qdf does not behave as expected'
assert numpy.fabs(sqrt(df.sigmaz2(0.9,0.05))-0.094215523962105044) < 10.**-4., 'qdf does not behave as expected'
# Calculate the tilt of the velocity ellipsoid
df.tilt(0.9,0.05)
assert numpy.fabs(df.tilt(0.9,0.05)-2.5166061974413765) < 10.**-4., 'qdf does not behave as expected'
# Calculate a higher-order moment of the velocity DF
df.vmomentdensity(0.9,0.05,6.,2.,2.,gl=True)
assert numpy.fabs(df.vmomentdensity(0.9,0.05,6.,2.,2.,gl=True)-0.0001591100892366438) < 10.**-4., 'qdf does not behave as expected'
# Sample velocities at given R,z, check mean
numpy.random.seed(1)
vs= df.sampleV(0.9,0.05,n=500); mvt= numpy.mean(vs[:,1])
assert numpy.fabs(numpy.mean(vs[:,0])) < 0.05 # vR
assert numpy.fabs(mvt-df.meanvT(0.9,0.05)) < 0.01 #vT
assert numpy.fabs(numpy.mean(vs[:,2])) < 0.05 # vz
return None
def test_tilt_adiabatic_gl():
qdf= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
pot=MWPotential,aA=aAA,cutcounter=True)
#should be zero everywhere
assert numpy.fabs(qdf.tilt(0.9,0.,gl=True)) < 0.05, "qdf's tilt deviates more than expected from zero for adiabatic approx."
assert numpy.fabs(qdf.tilt(0.9,0.2,gl=True)) < 0.05, "qdf's tilt deviates more than expected from zero for adiabatic approx."
assert numpy.fabs(qdf.tilt(0.9,-0.25,gl=True)) < 0.05, "qdf's tilt deviates more than expected from zero for adiabatic approx."
return None
def test_jmomentdensity_physical():
# Test physical output of jmomentdensity
qdf= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
pot=MWPotential,aA=aAS,cutcounter=True)
ro,vo= 7.,230.
assert numpy.fabs(qdf.jmomentdensity(1.1,0.1,0,0,0,nmc=100000,ro=ro,vo=vo)-qdf.jmomentdensity(1.1,0.1,0,0,0,nmc=100000)/ro**3*(ro*vo)**0) < 10.**-4., 'quasiisothermaldf method jmomentdensity does not return correct Quantity'
assert numpy.fabs(qdf.jmomentdensity(1.1,0.1,1,0,0,nmc=100000,ro=ro,vo=vo,use_physical=True)-qdf.jmomentdensity(1.1,0.1,1,0,0,nmc=100000)/ro**3*(ro*vo)**1) < 10.**-2., 'quasiisothermaldf method jmomentdensity does not return correct Quantity'
return None
def test_tilt_staeckel_mc():
numpy.random.seed(1)
qdf= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
pot=MWPotential,aA=aAS,cutcounter=True)
#should be zero in the mid-plane and roughly toward the GC elsewhere
assert numpy.fabs(qdf.tilt(0.9,0.,mc=True)) < 1., "qdf's tilt deviates more than expected from zero in the mid-plane for staeckel approx." #this is tough
assert numpy.fabs(qdf.tilt(0.9,0.1,mc=True)-numpy.arctan(0.1/0.9)/numpy.pi*180.) < 3., "qdf's tilt deviates more than expected from expected for staeckel approx."
return None
def test_jmomentdensity_physical():
# Test physical output of jmomentdensity
qdf= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
pot=MWPotential,aA=aAS,cutcounter=True)
ro,vo= 7.,230.
assert numpy.fabs(qdf.jmomentdensity(1.1,0.1,0,0,0,nmc=100000,ro=ro,vo=vo)-qdf.jmomentdensity(1.1,0.1,0,0,0,nmc=100000)/ro**3*(ro*vo)**0) < 10.**-4., 'quasiisothermaldf method jmomentdensity does not return correct Quantity'
assert numpy.fabs(qdf.jmomentdensity(1.1,0.1,1,0,0,nmc=100000,ro=ro,vo=vo,use_physical=True)-qdf.jmomentdensity(1.1,0.1,1,0,0,nmc=100000)/ro**3*(ro*vo)**1) < 10.**-2., 'quasiisothermaldf method jmomentdensity does not return correct Quantity'
return None
def test_sigmar_staeckel_gl():
qdf= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
pot=MWPotential,aA=aAS,cutcounter=True)
#In the mid-plane
assert numpy.fabs(numpy.log(qdf.sigmaR2(0.9,0.,gl=True))-2.*numpy.log(0.2)-0.2) < 0.2, "qdf's sigmaR2 deviates more than expected from input for staeckel approx."
#higher up, also w/ different ngl
assert numpy.fabs(numpy.log(qdf.sigmaR2(0.9,0.2,gl=True,ngl=20))-2.*numpy.log(0.2)-0.2) < 0.3, "qdf's sigmaR2 deviates more than expected from input for staeckel approx."
assert numpy.fabs(numpy.log(qdf.sigmaR2(0.9,-0.25,gl=True,ngl=24))-2.*numpy.log(0.2)-0.2) < 0.3, "qdf's sigmaR2 deviates more than expected from input for staeckel approx."
return None
def test_vmomentdensity_physical():
# Test physical output of vmomentdensity
qdf= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
pot=MWPotential,aA=aAS,cutcounter=True)
R, z= 0.8, 0.1
ro,vo= 7.,230.
assert numpy.fabs(qdf.vmomentdensity(R,z,0,0,0,gl=True,ngl=12,ro=ro,vo=vo)-qdf.vmomentdensity(R,z,0,0,0,gl=True,ngl=12)/ro**3) < 10.**-8., 'vmomentdensity with use_physical does not correspond to vmomentdensity without physical'
assert numpy.fabs(qdf.vmomentdensity(R,z,0,1,0,gl=True,ngl=12,ro=ro,vo=vo)-qdf.vmomentdensity(R,z,0,1,0,gl=True,ngl=12)*vo/ro**3) < 10.**-8., 'vmomentdensity with use_physical does not correspond to vmomentdensity without physical'
return None
def test_tilt_staeckel_gl():
qdf= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
pot=MWPotential,aA=aAS,cutcounter=True)
#should be zero in the mid-plane and roughly toward the GC elsewhere
assert numpy.fabs(qdf.tilt(0.9,0.,gl=True)) < 0.05, "qdf's tilt deviates more than expected from zero in the mid-plane for staeckel approx."
assert numpy.fabs(qdf.tilt(0.9,0.1,gl=True)-numpy.arctan(0.1/0.9)/numpy.pi*180.) < 2., "qdf's tilt deviates more than expected from expected for staeckel approx."
assert numpy.fabs(qdf.tilt(0.9,-0.15,gl=True)-numpy.arctan(-0.15/0.9)/numpy.pi*180.) < 2.5, "qdf's tilt deviates more than expected from expected for staeckel approx."
assert numpy.fabs(qdf.tilt(0.9,-0.25,gl=True)-numpy.arctan(-0.25/0.9)/numpy.pi*180.) < 4., "qdf's tilt deviates more than expected from expected for staeckel approx."
return None
def test_vmomentdensity_physical():
# Test physical output of vmomentdensity
qdf= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
pot=MWPotential,aA=aAS,cutcounter=True)
R, z= 0.8, 0.1
ro,vo= 7.,230.
assert numpy.fabs(qdf.vmomentdensity(R,z,0,0,0,gl=True,ngl=12,ro=ro,vo=vo)-qdf.vmomentdensity(R,z,0,0,0,gl=True,ngl=12)/ro**3) < 10.**-8., 'vmomentdensity with use_physical does not correspond to vmomentdensity without physical'
assert numpy.fabs(qdf.vmomentdensity(R,z,0,1,0,gl=True,ngl=12,ro=ro,vo=vo)-qdf.vmomentdensity(R,z,0,1,0,gl=True,ngl=12)*vo/ro**3) < 10.**-8., 'vmomentdensity with use_physical does not correspond to vmomentdensity without physical'
return None
def test_estimate_hz():
qdf= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
pot=MWPotential,aA=aAS,cutcounter=True)
from scipy import integrate
from galpy.potential import evaluateDensities
expec_hz= 0.1**2./2.\
/integrate.quad(lambda x: evaluateDensities(0.9,x,MWPotential),
0.,0.125)[0]/2./numpy.pi
assert numpy.fabs((qdf.estimate_hz(0.9,z=0.125)-expec_hz)/expec_hz) < 0.1, 'estimated scale height not as expected'
assert qdf.estimate_hz(0.9,z=0.) > 1., 'estimated scale height at z=0 not very large'
#Another one
qdf= quasiisothermaldf(1./4.,0.3,0.2,1.,1.,
pot=MWPotential,aA=aAS,cutcounter=True)
expec_hz= 0.2**2./2.\
/integrate.quad(lambda x: evaluateDensities(0.9,x,MWPotential),
0.,0.125)[0]/2./numpy.pi
assert numpy.fabs((qdf.estimate_hz(0.9,z=0.125)-expec_hz)/expec_hz) < 0.15, 'estimated scale height not as expected'
return None
def test_meanvR_staeckel_gl():
qdf= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
pot=MWPotential,aA=aAS,cutcounter=True)
#In the mid-plane
assert numpy.fabs(qdf.meanvR(0.9,0.,gl=True)) < 0.01, "qdf's meanvr is not equal to zero for staeckel approx."
#higher up
assert numpy.fabs(qdf.meanvR(0.9,0.2,gl=True)) < 0.01, "qdf's meanvr is not equal to zero for staeckel approx."
assert numpy.fabs(qdf.meanvR(0.9,-0.25,gl=True)) < 0.01, "qdf's meanvr is not equal to zero for staeckel approx."
return None
def test_sigmar_staeckel_mc():
numpy.random.seed(1)
qdf= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
pot=MWPotential,aA=aAS,cutcounter=True)
#In the mid-plane
assert numpy.fabs(numpy.log(qdf.sigmaR2(0.9,0.,mc=True))-2.*numpy.log(0.2)-0.2) < 0.2, "qdf's sigmaR2 deviates more than expected from input for staeckel approx."
#higher up
assert numpy.fabs(numpy.log(qdf.sigmaR2(0.9,0.2,mc=True))-2.*numpy.log(0.2)-0.2) < 0.4, "qdf's sigmaR2 deviates more than expected from input for staeckel approx."
assert numpy.fabs(numpy.log(qdf.sigmaR2(0.9,-0.25,mc=True))-2.*numpy.log(0.2)-0.2) < 0.3, "qdf's sigmaR2 deviates more than expected from input for staeckel approx."
return None
def test_sigmat_staeckel_mc():
numpy.random.seed(2)
qdf= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
pot=MWPotential,aA=aAS,cutcounter=True)
#In the mid-plane
gamma= 2.*omegac(MWPotential,0.9)/epifreq(MWPotential,0.9)
assert numpy.fabs(numpy.log(qdf.sigmaT2(0.9,0.,mc=True)/qdf.sigmaR2(0.9,0.,mc=True))+2.*numpy.log(gamma)) < 0.3, "qdf's sigmaT2/sigmaR2 deviates more than expected from input for staeckel approx."
#higher up
assert numpy.fabs(numpy.log(qdf.sigmaT2(0.9,0.2,mc=True)/qdf.sigmaR2(0.9,0.2,mc=True))+2.*numpy.log(gamma)) < 0.3, "qdf's sigmaT2/sigmaR2 deviates more than expected from input for staeckel approx."
return None
def test_meanvR_adiabatic_mc():
numpy.random.seed(1)
qdf= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
pot=MWPotential,aA=aAA,cutcounter=True)
#In the mid-plane
assert numpy.fabs(qdf.meanvR(0.9,0.,mc=True)) < 0.01, "qdf's meanvr is not equal to zero for adiabatic approx."
#higher up
assert numpy.fabs(qdf.meanvR(0.9,0.2,mc=True)) < 0.05, "qdf's meanvr is not equal to zero for adiabatic approx."
assert numpy.fabs(qdf.meanvR(0.9,-0.25,mc=True)) < 0.05, "qdf's meanvr is not equal to zero for adiabatic approx."
return None
def test_meanjr():
#This is a *very* rough test against a rough estimate of the mean
qdf= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
pot=MWPotential,aA=aAS,cutcounter=True)
assert numpy.fabs(numpy.log(qdf.meanjr(0.9,0.,mc=True))\
-2.*numpy.log(0.2)-0.2
+numpy.log(epifreq(MWPotential,0.9))) < 0.4, 'meanjr is not what is expected'
assert numpy.fabs(numpy.log(qdf.meanjr(0.5,0.,mc=True))\
-2.*numpy.log(0.2)-1.
+numpy.log(epifreq(MWPotential,0.5))) < 0.4, 'meanjr is not what is expected'
return None
def test_meanlz():
#This is a *very* rough test against a rough estimate of the mean
qdf= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
pot=MWPotential,aA=aAS,cutcounter=True)
from galpy.df import dehnendf #baseline
dfc= dehnendf(profileParams=(1./4.,1.0, 0.2),
beta=0.,correct=False)
assert numpy.fabs(numpy.log(qdf.meanlz(0.9,0.,mc=True))\
-numpy.log(0.9*dfc.meanvT(0.9))) < 0.1, 'meanlz is not what is expected'
assert numpy.fabs(numpy.log(qdf.meanlz(0.5,0.,mc=True))\
-numpy.log(0.5*dfc.meanvT(0.5))) < 0.2, 'meanlz is not what is expected'
return None
def test_pvT_staeckel():
# Test pvT by calculating its mean and stddev by Riemann sum
qdf= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
pot=MWPotential,aA=aAS,cutcounter=True)
R,z= 0.8, 0.1
vTs= numpy.linspace(0.,1.5,101)
pvT= numpy.array([qdf.pvT(vt,R,z) for vt in vTs])
mvT= numpy.sum(vTs*pvT)/numpy.sum(pvT)
svT= numpy.sqrt(numpy.sum(vTs**2.*pvT)/numpy.sum(pvT)-mvT**2.)
assert numpy.fabs(mvT-qdf.meanvT(R,z)) < 0.01, 'mean vT calculated from pvT not equal to zero for staeckel actions'
assert numpy.fabs(numpy.log(svT)-0.5*numpy.log(qdf.sigmaT2(R,z))) < 0.01, 'sigma vT calculated from pvT not equal to that from sigmaT2 for staeckel actions'
return None
def test_meanjz_noaac_issue300():
# Test of issue 300 reported by Ruth Angus: failure of qdf.meanjz when not using C integration for action-angle calculations
taA= actionAngleAdiabatic(pot=MWPotential,c=False)
hr= 1/3.
sr= .2
sz= .1
hsr= 1.
hsz= 1.
qdf= quasiisothermaldf(hr,sr,sz,hsr,hsz,pot=MWPotential,aA=taA,
cutcounter=True)
assert numpy.fabs(qdf.meanjz(1.,0.125,nmc=100)-0.0157468008111) < 0.01, 'Mean Jz computed using MC with Python actionAngleAdiabatic integration fails'
return None
def test_pvz_adiabatic():
# Test pvz by calculating its mean and stddev by Riemann sum
qdf= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
pot=MWPotential,aA=aAA,cutcounter=True)
R,z= 0.8, 0.1
vzs= numpy.linspace(-1.,1.,51)
pvz= numpy.array([qdf.pvz(vz,R,z) for vz in vzs])
mvz= numpy.sum(vzs*pvz)/numpy.sum(pvz)
svz= numpy.sqrt(numpy.sum(vzs**2.*pvz)/numpy.sum(pvz)-mvz**2.)
assert numpy.fabs(mvz) < 0.01, 'mean vz calculated from pvz not equal to zero for adiabatic actions'
assert numpy.fabs(numpy.log(svz)-0.5*numpy.log(qdf.sigmaz2(R,z))) < 0.01, 'sigma vz calculated from pvz not equal to that from sigmaz2 for adiabatic actions'
return None
def test_meanjz():
#This is a *very* rough test against a rough estimate of the mean
qdf= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
pot=MWPotential,aA=aAS,cutcounter=True)
ldiff= numpy.log(qdf.meanjz(0.9,0.,mc=True))-2.*numpy.log(0.1)-0.2\
+numpy.log(verticalfreq(MWPotential,0.9))
#expect this to be smaller than the rough estimate, but not by more than an order of magnitude
assert ldiff > -1. and ldiff < 0., 'meanjz is not what is expected'
ldiff= numpy.log(qdf.meanjz(0.5,0.,mc=True))-2.*numpy.log(0.1)-1.0\
+numpy.log(verticalfreq(MWPotential,0.5))
assert ldiff > -1. and ldiff < 0., 'meanjz is not what is expected'
return None
def test_pvR_staeckel():
# Test pvR by calculating its mean and stddev by Riemann sum
qdf= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
pot=MWPotential,aA=aAS,cutcounter=True)
R,z= 0.8, 0.1
vRs= numpy.linspace(-1.,1.,51)
pvR= numpy.array([qdf.pvR(vr,R,z) for vr in vRs])
mvR= numpy.sum(vRs*pvR)/numpy.sum(pvR)
svR= numpy.sqrt(numpy.sum(vRs**2.*pvR)/numpy.sum(pvR)-mvR**2.)
assert numpy.fabs(mvR) < 0.01, 'mean vR calculated from pvR not equal to zero for staeckel actions'
assert numpy.fabs(numpy.log(svR)-0.5*numpy.log(qdf.sigmaR2(R,z))) < 0.01, 'sigma vR calculated from pvR not equal to that from sigmaR2 for staeckel actions'
return None
def test_meanjz_noaac_issue300():
# Test of issue 300 reported by Ruth Angus: failure of qdf.meanjz when not using C integration for action-angle calculations
taA= actionAngleAdiabatic(pot=MWPotential,c=False)
hr= 1/3.
sr= .2
sz= .1
hsr= 1.
hsz= 1.
qdf= quasiisothermaldf(hr,sr,sz,hsr,hsz,pot=MWPotential,aA=taA,
cutcounter=True)
assert numpy.fabs(qdf.meanjz(1.,0.125,nmc=100)-0.0157468008111) < 0.01, 'Mean Jz computed using MC with Python actionAngleAdiabatic integration fails'
return None
def test_jmomentdensity_diffinoutputs():
qdf= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
pot=MWPotential,aA=aAS,cutcounter=True)
#Some tests of mc=True
R,z= 0.8, 0.1
jr2surfmass, vrs, vts, vzs= qdf.jmomentdensity(R,z,2.,0.,0.,mc=True,
_returnmc=True)
assert numpy.fabs(numpy.log(jr2surfmass)-numpy.log(qdf.jmomentdensity(R,z,2.,0.,0.,
mc=True,
_vrs=vrs,
_vts=vts,
_vzs=vzs))) < 0.0001, 'qdf.jmomentdensity w/ rawgausssamples and mc=True does not agree with that w/o rawgausssamples'
return None
def quasiisothermal(samples, params=[1./0.3,0.2,0.1,1./1.,1./1.]):
qdf= quasiisothermaldf(1./3.,params[1],params[2],1./params[3],1./params[4],
pot=MWPotential2014,aA=aAS,cutcounter=True)
mask = np.all(np.isfinite(samples), axis=1)
samples = samples[mask]
dens = qdf(samples[:,0], samples[:,1], samples[:,2], samples[:,3], samples[:,4])
dens[dens < 1e-30] = 1e-30
out = np.sum(np.log(dens))
print(out)
print(params)
return out
def test_sigmaz_staeckel_gl():
qdf= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
pot=MWPotential,aA=aAS,cutcounter=True)
#In the mid-plane
assert numpy.fabs(numpy.log(qdf.sigmaz2(0.9,0.,gl=True))-2.*numpy.log(0.1)-0.2) < 0.5, "qdf's sigmaz2 deviates more than expected from input for staeckel approx."
#from Bovy & Rix 2013, we know that this has to be smaller
assert numpy.log(qdf.sigmaz2(0.9,0.,gl=True)) < 2.*numpy.log(0.1)+0.2 < 0.5, "qdf's sigmaz2 deviates more than expected from input for staeckel approx."
#higher up
assert numpy.fabs(numpy.log(qdf.sigmaz2(0.9,0.2,gl=True))-2.*numpy.log(0.1)-0.2) < 0.5, "qdf's sigmaz2 deviates more than expected from input for staeckel approx."
assert numpy.log(qdf.sigmaz2(0.9,0.2,gl=True)) < 2.*numpy.log(0.1)+0.2 < 0.5, "qdf's sigmaz2 deviates more than expected from input for staeckel approx."
assert numpy.fabs(numpy.log(qdf.sigmaz2(0.9,-0.25,gl=True))-2.*numpy.log(0.1)-0.2) < 0.5, "qdf's sigmaz2 deviates more than expected from input for staeckel approx."
assert numpy.log(qdf.sigmaz2(0.9,-0.25,gl=True)) < 2.*numpy.log(0.1)+0.2 < 0.5, "qdf's sigmaz2 deviates more than expected from input for staeckel approx."
return None
def test_meanvT_staeckel_gl():
qdf= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
pot=MWPotential,aA=aAS,cutcounter=True)
from galpy.df import dehnendf #baseline
dfc= dehnendf(profileParams=(1./4.,1.0, 0.2),
beta=0.,correct=False)
#In the mid-plane
vtp9= qdf.meanvT(0.9,0.,gl=True)
assert numpy.fabs(vtp9-dfc.meanvT(0.9)) < 0.05, "qdf's meanvT is not close to that of dehnendf"
assert vtp9 < vcirc(MWPotential,0.9), "qdf's meanvT is not less than the circular velocity (which we expect)"
#higher up
assert qdf.meanvR(0.9,0.2,gl=True) < vtp9, "qdf's meanvT above the plane is not less than in the plane (which we expect)"
assert qdf.meanvR(0.9,-0.25,gl=True) < vtp9, "qdf's meanvT above the plane is not less than in the plane (which we expect)"
return None
def test_sampleV():
qdf= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
pot=MWPotential,aA=aAS,cutcounter=True)
numpy.random.seed(1)
samples= qdf.sampleV(0.8,0.1,n=1000)
#test vR
assert numpy.fabs(numpy.mean(samples[:,0])) < 0.02, 'sampleV vR mean is not zero'
assert numpy.fabs(numpy.log(numpy.std(samples[:,0]))-0.5*numpy.log(qdf.sigmaR2(0.8,0.1))) < 0.05, 'sampleV vR stddev is not equal to sigmaR'
#test vT
assert numpy.fabs(numpy.mean(samples[:,1]-qdf.meanvT(0.8,0.1))) < 0.015, 'sampleV vT mean is not equal to meanvT'
assert numpy.fabs(numpy.log(numpy.std(samples[:,1]))-0.5*numpy.log(qdf.sigmaT2(0.8,0.1))) < 0.05, 'sampleV vT stddev is not equal to sigmaT'
#test vz
assert numpy.fabs(numpy.mean(samples[:,2])) < 0.01, 'sampleV vz mean is not zero'
assert numpy.fabs(numpy.log(numpy.std(samples[:,2]))-0.5*numpy.log(qdf.sigmaz2(0.8,0.1))) < 0.05, 'sampleV vz stddev is not equal to sigmaz'
return None
def test_overview():
from galpy.potential import NFWPotential
np= NFWPotential(normalize=1.)
from galpy.orbit import Orbit
o= Orbit(vxvv=[1.,0.1,1.1,0.1,0.02,0.])
from galpy.actionAngle import actionAngleSpherical
aA= actionAngleSpherical(pot=np)
js= aA(o)
assert numpy.fabs((js[0]-0.00980542)/js[0]) < 10.**-3., 'Action calculation in the overview section has changed'
assert numpy.fabs((js[1]-1.1)/js[0]) < 10.**-3., 'Action calculation in the overview section has changed'
assert numpy.fabs((js[2]-0.00553155)/js[0]) < 10.**-3., 'Action calculation in the overview section has changed'
from galpy.df import quasiisothermaldf
qdf= quasiisothermaldf(1./3.,0.2,0.1,1.,1.,
pot=np,aA=aA)
assert numpy.fabs((qdf(o)-61.57476085)/61.57476085) < 10.**-3., 'qdf calculation in the overview section has changed'
return None
def test_overview():
from galpy.potential import NFWPotential
np= NFWPotential(normalize=1.)
from galpy.orbit import Orbit
o= Orbit(vxvv=[1.,0.1,1.1,0.1,0.02,0.])
from galpy.actionAngle import actionAngleSpherical
aA= actionAngleSpherical(pot=np)
js= aA(o)
assert numpy.fabs((js[0]-0.00980542)/js[0]) < 10.**-3., 'Action calculation in the overview section has changed'
assert numpy.fabs((js[1]-1.1)/js[0]) < 10.**-3., 'Action calculation in the overview section has changed'
assert numpy.fabs((js[2]-0.00553155)/js[0]) < 10.**-3., 'Action calculation in the overview section has changed'
from galpy.df import quasiisothermaldf
qdf= quasiisothermaldf(1./3.,0.2,0.1,1.,1.,
pot=np,aA=aA)
assert numpy.fabs((qdf(o)-61.57476085)/61.57476085) < 10.**-3., 'qdf calculation in the overview section has changed'
return None
def test_vmomentdensity_diffinoutputs():
qdf= quasiisothermaldf(1./4.,0.2,0.1,1.,1.,
pot=MWPotential,aA=aAS,cutcounter=True)
#Test that we can input use different ngl
R, z= 0.8, 0.1
sigmar2= qdf.sigmaR2(R,z,gl=True)
assert numpy.fabs(numpy.log(qdf.sigmaR2(R,z,gl=True,_sigmaR1=0.95*numpy.sqrt(qdf.sigmaR2(R,z)),_sigmaz1=0.95*numpy.sqrt(qdf.sigmaz2(R,z))))-numpy.log(sigmar2)) < 0.01, 'sigmaR2 calculated w/ explicit sigmaR1 and sigmaz1 do not agree'
#Test ngl inputs further
try:
qdf.vmomentdensity(R,z,0,0,0,gl=True,ngl=11)
except ValueError: pass
else: raise AssertionError('qdf.vmomentdensity w/ ngl == odd does not raise ValueError')
surfmass, glqeval= qdf.vmomentdensity(R,z,0.,0.,0.,
gl=True,
_returngl=True)
#This shouldn't reuse gleval, but should work nonetheless
assert numpy.fabs(numpy.log(surfmass)\
-numpy.log(qdf.vmomentdensity(R,z,0.,0.,0.,
gl=True,
_glqeval=glqeval,
ngl=30))) < 0.05, 'vmomentsurfmass w/ wrong glqeval input does not work'
#Test that we can re-use jr, etc.
surfmass, jr,lz,jz= qdf.vmomentdensity(R,z,0.,0.,0.,gl=True,
_return_actions=True)
assert numpy.fabs(numpy.log(surfmass)\
-numpy.log(qdf.vmomentdensity(R,z,0.,0.,0.,gl=True,
_jr=jr,_lz=lz,_jz=jz))) < 0.01, 'surfacemass calculated from re-used actions does not agree with that before'
surfmass, jr,lz,jz, rg, kappa, nu, Omega=\
qdf.vmomentdensity(R,z,0.,0.,0.,gl=True,
_return_actions=True,
_return_freqs=True)
assert numpy.fabs(numpy.log(surfmass)\
-numpy.log(qdf.vmomentdensity(R,z,0.,0.,0.,gl=True,
_jr=jr,_lz=lz,_jz=jz,
_rg=rg,_kappa=kappa,
_nu=nu,_Omega=Omega))) < 0.01, 'surfacemass calculated from re-used actions does not agree with that before'
#Some tests of mc=True
surfmass, vrs, vts, vzs= qdf.vmomentdensity(R,z,0.,0.,0.,mc=True,gl=False,
_rawgausssamples=True,
_returnmc=True)
assert numpy.fabs(numpy.log(surfmass)-numpy.log(qdf.vmomentdensity(R,z,0.,0.,0.,
mc=True,gl=False,
_rawgausssamples=True,
_vrs=vrs,
_vts=vts,
_vzs=vzs))) < 0.0001, 'qdf.vmomentdensity w/ rawgausssamples and mc=True does not agree with that w/o rawgausssamples'
return None
