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_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_diskdf():
from galpy.df import dehnendf
# Init. dehnendf w/ flat rot., hr=1/3, hs=1, and sr(1)=0.2
df = dehnendf(beta=0., profileParams=(1. / 3., 1.0, 0.2))
# Same, w/ correction factors to scale profiles
dfc = dehnendf(beta=0.,
profileParams=(1. / 3., 1.0, 0.2),
correct=True,
niter=20)
if True:
# Log. diff. between scale and DF surf. dens.
numpy.log(df.surfacemass(0.5) / df.targetSurfacemass(0.5))
assert numpy.fabs(
numpy.log(df.surfacemass(0.5) / df.targetSurfacemass(0.5)) +
0.056954077791649592
) < 10.**-4., 'diskdf does not behave as expected'
# Same for corrected DF
numpy.log(dfc.surfacemass(0.5) / dfc.targetSurfacemass(0.5))
assert numpy.fabs(
numpy.log(dfc.surfacemass(0.5) / dfc.targetSurfacemass(0.5)) +
4.1440377205802041e-06
) < 10.**-4., 'diskdf does not behave as expected'
# Log. diff between scale and DF sr
numpy.log(df.sigmaR2(0.5) / df.targetSigma2(0.5))
assert numpy.fabs(
numpy.log(df.sigmaR2(0.5) / df.targetSigma2(0.5)) +
0.12786083001363127
) < 10.**-4., 'diskdf does not behave as expected'
# Same for corrected DF
numpy.log(dfc.sigmaR2(0.5) / dfc.targetSigma2(0.5))
assert numpy.fabs(
numpy.log(dfc.sigmaR2(0.5) / dfc.targetSigma2(0.5)) +
6.8065001252214986e-06
) < 10.**-4., 'diskdf does not behave as expected'
# Evaluate DF w/ R,vR,vT
df(numpy.array([0.9, 0.1, 0.8]))
assert numpy.fabs(
df(numpy.array([0.9, 0.1, 0.8])) - numpy.array(0.1740247246180417)
) < 10.**-4., 'diskdf does not behave as expected'
# Evaluate corrected DF w/ Orbit instance
from galpy.orbit import Orbit
dfc(Orbit([0.9, 0.1, 0.8]))
assert numpy.fabs(
dfc(Orbit([0.9, 0.1, 0.8])) - numpy.array(0.16834863725552207)
) < 10.**-4., 'diskdf does not behave as expected'
# Calculate the mean velocities
df.meanvR(0.9), df.meanvT(0.9)
assert numpy.fabs(
df.meanvR(0.9)) < 10.**-4., 'diskdf does not behave as expected'
assert numpy.fabs(df.meanvT(0.9) - 0.91144428051168291
) < 10.**-4., 'diskdf does not behave as expected'
# Calculate the velocity dispersions
numpy.sqrt(dfc.sigmaR2(0.9)), numpy.sqrt(dfc.sigmaT2(0.9))
assert numpy.fabs(numpy.sqrt(dfc.sigmaR2(0.9)) - 0.22103383792719539
) < 10.**-4., 'diskdf does not behave as expected'
assert numpy.fabs(numpy.sqrt(dfc.sigmaT2(0.9)) - 0.17613725303902811
) < 10.**-4., 'diskdf does not behave as expected'
# Calculate the skew of the velocity distribution
df.skewvR(0.9), df.skewvT(0.9)
assert numpy.fabs(
df.skewvR(0.9)) < 10.**-4., 'diskdf does not behave as expected'
assert numpy.fabs(df.skewvT(0.9) + 0.47331638366025863
) < 10.**-4., 'diskdf does not behave as expected'
# Calculate the kurtosis of the velocity distribution
df.kurtosisvR(0.9), df.kurtosisvT(0.9)
assert numpy.fabs(df.kurtosisvR(0.9) + 0.13561300880237059
) < 10.**-4., 'diskdf does not behave as expected'
assert numpy.fabs(df.kurtosisvT(0.9) - 0.12612702099300721
) < 10.**-4., 'diskdf does not behave as expected'
# Calculate a higher-order moment of the velocity DF
df.vmomentsurfacemass(1., 6., 2.) / df.surfacemass(1.)
assert numpy.fabs(
df.vmomentsurfacemass(1., 6., 2.) / df.surfacemass(1.) -
0.00048953492205559054
) < 10.**-4., 'diskdf does not behave as expected'
# Calculate the Oort functions
dfc.oortA(1.), dfc.oortB(1.), dfc.oortC(1.), dfc.oortK(1.)
assert numpy.fabs(dfc.oortA(1.) - 0.40958989067012197
) < 10.**-4., 'diskdf does not behave as expected'
assert numpy.fabs(dfc.oortB(1.) + 0.49396172114486514
) < 10.**-4., 'diskdf does not behave as expected'
assert numpy.fabs(
dfc.oortC(1.)) < 10.**-4., 'diskdf does not behave as expected'
assert numpy.fabs(
dfc.oortK(1.)) < 10.**-4., 'diskdf does not behave as expected'
# Sample Orbits from the DF, returns list of Orbits
numpy.random.seed(1)
os = dfc.sample(n=100, returnOrbit=True, nphi=1)
# check that these have the right mean radius = 2hr=2/3
rs = numpy.array([o.R() for o in os])
assert numpy.fabs(numpy.mean(rs) - 2. / 3.) < 0.1
# Sample vR and vT at given R, check their mean
vrvt = dfc.sampleVRVT(0.7, n=500, target=True)
vt = vrvt[:, 1]
assert numpy.fabs(numpy.mean(vrvt[:, 0])) < 0.05
assert numpy.fabs(numpy.mean(vt) - dfc.meanvT(0.7)) < 0.01
# Sample Orbits along a given line-of-sight
os = dfc.sampleLOS(45., n=1000)
return None
def test_diskdf():
from galpy.df import dehnendf
# Init. dehnendf w/ flat rot., hr=1/3, hs=1, and sr(1)=0.2
df= dehnendf(beta=0.,profileParams=(1./3.,1.0,0.2))
# Same, w/ correction factors to scale profiles
dfc= dehnendf(beta=0.,profileParams=(1./3.,1.0,0.2),
correct=True,niter=20)
if True:
# Log. diff. between scale and DF surf. dens.
numpy.log(df.surfacemass(0.5)/df.targetSurfacemass(0.5))
assert numpy.fabs(numpy.log(df.surfacemass(0.5)/df.targetSurfacemass(0.5))+0.056954077791649592) < 10.**-4., 'diskdf does not behave as expected'
# Same for corrected DF
numpy.log(dfc.surfacemass(0.5)/dfc.targetSurfacemass(0.5))
assert numpy.fabs(numpy.log(dfc.surfacemass(0.5)/dfc.targetSurfacemass(0.5))+4.1440377205802041e-06) < 10.**-4., 'diskdf does not behave as expected'
# Log. diff between scale and DF sr
numpy.log(df.sigmaR2(0.5)/df.targetSigma2(0.5))
assert numpy.fabs(numpy.log(df.sigmaR2(0.5)/df.targetSigma2(0.5))+0.12786083001363127) < 10.**-4., 'diskdf does not behave as expected'
# Same for corrected DF
numpy.log(dfc.sigmaR2(0.5)/dfc.targetSigma2(0.5))
assert numpy.fabs(numpy.log(dfc.sigmaR2(0.5)/dfc.targetSigma2(0.5))+6.8065001252214986e-06) < 10.**-4., 'diskdf does not behave as expected'
# Evaluate DF w/ R,vR,vT
df(numpy.array([0.9,0.1,0.8]))
assert numpy.fabs(df(numpy.array([0.9,0.1,0.8]))-numpy.array(0.1740247246180417)) < 10.**-4., 'diskdf does not behave as expected'
# Evaluate corrected DF w/ Orbit instance
from galpy.orbit import Orbit
dfc(Orbit([0.9,0.1,0.8]))
assert numpy.fabs(dfc(Orbit([0.9,0.1,0.8]))-numpy.array(0.16834863725552207)) < 10.**-4., 'diskdf does not behave as expected'
# Calculate the mean velocities
df.meanvR(0.9), df.meanvT(0.9)
assert numpy.fabs(df.meanvR(0.9)) < 10.**-4., 'diskdf does not behave as expected'
assert numpy.fabs(df.meanvT(0.9)-0.91144428051168291) < 10.**-4., 'diskdf does not behave as expected'
# Calculate the velocity dispersions
numpy.sqrt(dfc.sigmaR2(0.9)), numpy.sqrt(dfc.sigmaT2(0.9))
assert numpy.fabs(numpy.sqrt(dfc.sigmaR2(0.9))-0.22103383792719539) < 10.**-4., 'diskdf does not behave as expected'
assert numpy.fabs(numpy.sqrt(dfc.sigmaT2(0.9))-0.17613725303902811) < 10.**-4., 'diskdf does not behave as expected'
# Calculate the skew of the velocity distribution
df.skewvR(0.9), df.skewvT(0.9)
assert numpy.fabs(df.skewvR(0.9)) < 10.**-4., 'diskdf does not behave as expected'
assert numpy.fabs(df.skewvT(0.9)+0.47331638366025863) < 10.**-4., 'diskdf does not behave as expected'
# Calculate the kurtosis of the velocity distribution
df.kurtosisvR(0.9), df.kurtosisvT(0.9)
assert numpy.fabs(df.kurtosisvR(0.9)+0.13561300880237059) < 10.**-4., 'diskdf does not behave as expected'
assert numpy.fabs(df.kurtosisvT(0.9)-0.12612702099300721) < 10.**-4., 'diskdf does not behave as expected'
# Calculate a higher-order moment of the velocity DF
df.vmomentsurfacemass(1.,6.,2.)/df.surfacemass(1.)
assert numpy.fabs(df.vmomentsurfacemass(1.,6.,2.)/df.surfacemass(1.)-0.00048953492205559054) < 10.**-4., 'diskdf does not behave as expected'
# Calculate the Oort functions
dfc.oortA(1.), dfc.oortB(1.), dfc.oortC(1.), dfc.oortK(1.)
assert numpy.fabs(dfc.oortA(1.)-0.40958989067012197) < 10.**-4., 'diskdf does not behave as expected'
assert numpy.fabs(dfc.oortB(1.)+0.49396172114486514) < 10.**-4., 'diskdf does not behave as expected'
assert numpy.fabs(dfc.oortC(1.)) < 10.**-4., 'diskdf does not behave as expected'
assert numpy.fabs(dfc.oortK(1.)) < 10.**-4., 'diskdf does not behave as expected'
# Sample Orbits from the DF, returns list of Orbits
numpy.random.seed(1)
os= dfc.sample(n=100,returnOrbit=True,nphi=1)
# check that these have the right mean radius = 2hr=2/3
rs= numpy.array([o.R() for o in os])
assert numpy.fabs(numpy.mean(rs)-2./3.) < 0.1
# Sample vR and vT at given R, check their mean
vrvt= dfc.sampleVRVT(0.7,n=500,target=True); vt= vrvt[:,1]
assert numpy.fabs(numpy.mean(vrvt[:,0])) < 0.05
assert numpy.fabs(numpy.mean(vt)-dfc.meanvT(0.7)) < 0.01
# Sample Orbits along a given line-of-sight
os= dfc.sampleLOS(45.,n=1000)
return None
