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