Exemplo n.º 1
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def test_mildnonaxi_meanvr_grid_tlist():
    # Test that for a close to axisymmetric potential, the mean vr is close to zero
    idf = dehnendf(beta=0.)
    pot = [
        LogarithmicHaloPotential(normalize=1.),
        SteadyLogSpiralPotential(A=-0.005, omegas=0.2)
    ]  #very mild non-axi
    edf = evolveddiskdf(idf, pot=pot, to=-10.)
    mvr, grid = edf.meanvR(0.9,
                           t=[0., -2.5, -5., -7.5, -10.],
                           phi=0.2,
                           integrate_method='rk6_c',
                           grid=True,
                           returnGrid=True,
                           gridpoints=_GRIDPOINTS)
    assert numpy.all(
        numpy.fabs(mvr) < 0.003
    ), 'meanvR of evolveddiskdf for axisymmetric potential is not equal to zero for list of times'
    mvr = edf.meanvR(0.9,
                     t=[0., -2.5, -5., -7.5, -10.],
                     phi=0.2,
                     integrate_method='rk6_c',
                     grid=grid)
    assert numpy.all(
        numpy.fabs(mvr) < 0.003
    ), 'meanvR of evolveddiskdf for axisymmetric potential is not equal to zero when calculated with pre-computed grid for list of times'
    return None
Exemplo n.º 2
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def test_elliptical_cold_vertexdev():
    # Test that the vertex deviations for the elliptical disk behaves as analytically expected
    idf = dehnendf(beta=0., profileParams=(1. / 3., 1., 0.0125))
    cp = 0.05
    pot = [
        LogarithmicHaloPotential(normalize=1.),
        EllipticalDiskPotential(cp=cp, sp=0., p=0., tform=-150., tsteady=125.)
    ]
    edf = evolveddiskdf(idf, pot=pot, to=-150.)
    #Should be -2cp in radians
    vdev, grid = edf.vertexdev(0.9,
                               phi=-numpy.pi / 4.,
                               integrate_method='rk6_c',
                               grid=True,
                               nsigma=7.,
                               returnGrid=True,
                               gridpoints=_GRIDPOINTS)
    assert numpy.fabs(
        vdev + 2. * cp
    ) < 10.**-3., 'Cold elliptical disk does not agree with analytical calculation for vertexdev'
    #Should be 0
    vdev, grid = edf.vertexdev(0.9,
                               phi=0.,
                               integrate_method='rk6_c',
                               grid=True,
                               nsigma=7.,
                               returnGrid=True,
                               gridpoints=_GRIDPOINTS)
    assert numpy.fabs(
        vdev
    ) < 10.**-2. / 180. * numpy.pi, 'Cold elliptical disk does not agree with analytical calculation for vertexdev'
    return None
Exemplo n.º 3
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def test_mildnonaxi_oortC_grid():
    # Test that for a close to axisymmetric potential, the oortC is close to zero
    idf = dehnendf(beta=0.)
    pot = [
        LogarithmicHaloPotential(normalize=1.),
        EllipticalDiskPotential(twophio=0.001)
    ]  #very mild non-axi
    edf = evolveddiskdf(idf, pot=pot, to=-10.)
    oc, grid, dgridR, dgridphi=\
        edf.oortC(0.9,phi=0.2,integrate_method='rk6_c',
                  grid=True,derivRGrid=True,derivphiGrid=True,
                  returnGrids=True,
                  gridpoints=_GRIDPOINTS,derivGridpoints=_GRIDPOINTS)
    assert numpy.fabs(
        oc
    ) < 0.005, 'oortC of evolveddiskdf for axisymmetric potential is not equal to that of initial DF'
    oc = edf.oortC(0.9,
                   phi=0.2,
                   integrate_method='rk6_c',
                   grid=grid,
                   derivRGrid=dgridR,
                   derivphiGrid=dgridphi,
                   gridpoints=_GRIDPOINTS,
                   derivGridpoints=_GRIDPOINTS)
    assert numpy.fabs(
        oc
    ) < 0.005, 'oortC of evolveddiskdf for axisymmetric potential is not equal to that of initial DF when calculated with pre-computed grid'
    return None
Exemplo n.º 4
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def test_mildnonaxi_meanvt_grid_tlist_onet():
    # Test that for a close to axisymmetric potential, the mean vt is close to that of the initial DF, for a list consisting of a single time
    idf = dehnendf(beta=0.)
    pot = [
        LogarithmicHaloPotential(normalize=1.),
        SteadyLogSpiralPotential(A=-0.005, omegas=0.2)
    ]  #very mild non-axi
    edf = evolveddiskdf(idf, pot=pot, to=-10.)
    mvt, grid = edf.meanvT(0.9,
                           t=[0.],
                           phi=0.2,
                           integrate_method='rk6_c',
                           grid=True,
                           returnGrid=True,
                           gridpoints=_GRIDPOINTS)
    assert numpy.fabs(
        mvt - idf.meanvT(0.9)
    ) < 0.005, 'meanvT of evolveddiskdf for axisymmetric potential is not equal to that of the initial dehnendf'
    mvt = edf.meanvT(
        0.9,
        t=[0.],
        phi=0.2,
        integrate_method='rk6_c',
        grid=grid,
        gridpoints=_GRIDPOINTS,
    )
    assert numpy.fabs(
        mvt - idf.meanvT(0.9)
    ) < 0.005, 'meanvT of evolveddiskdf for axisymmetric potential is not equal to that of the initial dehnendf when calculated with pre-computed grid'
    global _maxi_meanvt
    _maxi_meanvt = mvt
    return None
def test_mildnonaxi_meanvt_hierarchgrid():
    # Test that for a close to axisymmetric potential, the mean vt is close to that of the initial DF
    idf= dehnendf(beta=0.)
    pot= [LogarithmicHaloPotential(normalize=1.),
          SteadyLogSpiralPotential(A=-0.005,omegas=0.2)] #very mild non-axi
    edf= evolveddiskdf(idf,pot=pot,to=-10.)
    mvt, grid= edf.meanvT(0.9,phi=0.2,integrate_method='rk6_c',
                          grid=True,hierarchgrid=True,
                          returnGrid=True,gridpoints=_GRIDPOINTS)
    assert numpy.fabs(mvt-idf.meanvT(0.9)) < 0.005, 'meanvT of evolveddiskdf for axisymmetric potential is not equal to that of the initial dehnendf when using hierarchgrid'
    mvt= edf.meanvT(0.9,phi=0.2,integrate_method='rk6_c',grid=grid,
                    gridpoints=_GRIDPOINTS)
    assert numpy.fabs(mvt-idf.meanvT(0.9)) < 0.005, 'meanvT of evolveddiskdf for axisymmetric potential is not equal to that of the initial dehnendf when calculated with pre-computed grid when using hierarchgrid'
    #Also test that the hierarchgrid is properly returned
    smass, ngrid= edf.vmomentsurfacemass(0.9,0,0,phi=0.2,
                                        integrate_method='rk6_c',
                                        grid=grid,gridpoints=_GRIDPOINTS,
                                         returnGrid=True)
    assert ngrid == grid, 'hierarchical grid returned by vmomentsurfacemass w/ grid input is not the same as the input'
    nsmass= edf.vmomentsurfacemass(0.9,0,0,phi=0.2,
                                   integrate_method='rk6_c',
                                   grid=True,hierarchgrid=True,
                                   gridpoints=_GRIDPOINTS)
    assert numpy.fabs(smass-nsmass) < 0.001, 'surfacemass computed w/ and w/o returnGrid are not the same'
    return None
Exemplo n.º 6
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def test_mildnonaxi_meanvr_grid():
    # Test that for a close to axisymmetric potential, the mean vr is close to zero
    idf= dehnendf(beta=0.)
    pot= [LogarithmicHaloPotential(normalize=1.),
          SteadyLogSpiralPotential(A=-0.005,omegas=0.2)] #very mild non-axi
    edf= evolveddiskdf(idf,pot=pot,to=-10.)
    mvr, grid= edf.meanvR(0.9,phi=0.2,integrate_method='rk6_c',grid=True,
                          returnGrid=True,gridpoints=_GRIDPOINTS)
    assert numpy.fabs(mvr) < 0.001, 'meanvR of evolveddiskdf for axisymmetric potential is not equal to zero'
    mvr= edf.meanvR(0.9,phi=0.2,integrate_method='rk6_c',grid=grid)
    assert numpy.fabs(mvr) < 0.001, 'meanvR of evolveddiskdf for axisymmetric potential is not equal to zero when calculated with pre-computed grid'
    #Pre-compute surfmass and use it, first test that grid is properly returned when given
    smass, ngrid= edf.vmomentsurfacemass(0.9,0,0,phi=0.2,
                                        integrate_method='rk6_c',
                                        grid=grid,gridpoints=_GRIDPOINTS,
                                        returnGrid=True)
    assert ngrid == grid, 'grid returned by vmomentsurfacemass w/ grid input is not the same as the input'
    #Pre-compute surfmass and use it
    nsmass= edf.vmomentsurfacemass(0.9,0,0,phi=0.2,
                                   integrate_method='rk6_c',
                                   grid=True,gridpoints=_GRIDPOINTS)
    assert numpy.fabs(smass-nsmass) < 0.001, 'surfacemass computed w/ and w/o returnGrid are not the same'
    mvr= edf.meanvR(0.9,phi=0.2,integrate_method='rk6_c',grid=grid,
                    surfacemass=smass)
    assert numpy.fabs(mvr) < 0.001, 'meanvR of evolveddiskdf for axisymmetric potential is not equal to zero when calculated with pre-computed grid and surfacemass'
    global _maxi_meanvr
    _maxi_meanvr= mvr
    global _maxi_surfacemass
    _maxi_surfacemass= smass
    return None
Exemplo n.º 7
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def test_elliptical_cold_oortABCK_position2():
    # Test that the Oort functions A, B, C, and K for the elliptical disk behaves as analytically expected
    idf= dehnendf(beta=0.,profileParams=(1./3.,1.,0.0125))
    cp= 0.05
    pot= [LogarithmicHaloPotential(normalize=1.),
          EllipticalDiskPotential(cp=cp,sp=0.,p=0.,tform=-150.,tsteady=125.)]
    edf= evolveddiskdf(idf,pot=pot,to=-150.)
    #Should be 0.5/0.9+cp/2
    oorta, grid, gridr, gridp= edf.oortA(0.9,phi=0.,
                                         integrate_method='rk6_c',grid=True,
                                         nsigma=7.,
                                         derivRGrid=True,derivphiGrid=True,
                                         returnGrids=True,
                                         gridpoints=51,
                                         derivGridpoints=51)
    assert numpy.fabs(oorta-cp/2.-0.5/0.9) < 10.**-2.2, 'Cold elliptical disk does not agree with analytical calculation for oortA'
    #Should be -cp/2-0.5/0.9
    oortb= edf.oortB(0.9,phi=0.,
                     integrate_method='rk6_c',grid=grid,nsigma=7.,
                     derivRGrid=gridr,derivphiGrid=gridp)
    assert numpy.fabs(oortb+cp/2.+0.5/0.9) < 10.**-2.2, 'Cold elliptical disk does not agree with analytical calculation for oortB'
    #Should be 0
    oortc= edf.oortC(0.9,phi=0.,
                     integrate_method='rk6_c',grid=grid,nsigma=7.,
                     derivRGrid=gridr,derivphiGrid=gridp)
    assert numpy.fabs(oortc) < 10.**-3., 'Cold elliptical disk does not agree with analytical calculation for oortC'
    #Should be 0
    oortk= edf.oortK(0.9,phi=0.,
                     integrate_method='rk6_c',grid=grid,nsigma=7.,
                     derivRGrid=gridr,derivphiGrid=gridp)
    assert numpy.fabs(oortk) < 10.**-3., 'Cold elliptical disk does not agree with analytical calculation for oortK'
    return None
Exemplo n.º 8
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def test_mildnonaxi_oortA_grid_tlist():
    # Test that for a close to axisymmetric potential, the oortA is close to the value of the initial DF
    idf = dehnendf(beta=0.)
    pot = [
        LogarithmicHaloPotential(normalize=1.),
        EllipticalDiskPotential(twophio=0.001)
    ]  #very mild non-axi
    edf = evolveddiskdf(idf, pot=pot, to=-10.)
    oa, grid, dgridR, dgridphi=\
        edf.oortA(0.9,t=[0.,-2.5,-5.,-7.5,-10.],
                  phi=0.2,integrate_method='rk6_c',
                  grid=True,derivRGrid=True,derivphiGrid=True,
                  returnGrids=True,
                  gridpoints=_GRIDPOINTS,derivGridpoints=_GRIDPOINTS)
    ioa = idf.oortA(0.9)
    assert numpy.all(
        numpy.fabs(oa - ioa) < 0.005
    ), 'oortA of evolveddiskdf for axisymmetric potential is not equal to that of initial DF'
    oa = edf.oortA(0.9,
                   t=[0., -2.5, -5., -7.5, -10.],
                   phi=0.2,
                   integrate_method='rk6_c',
                   grid=grid,
                   derivRGrid=dgridR,
                   derivphiGrid=dgridphi,
                   gridpoints=_GRIDPOINTS,
                   derivGridpoints=_GRIDPOINTS)
    assert numpy.all(
        numpy.fabs(oa - ioa) < 0.005
    ), 'oortA of evolveddiskdf for axisymmetric potential is not equal to that of initial DF when calculated with pre-computed grid'
    return None
Exemplo n.º 9
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def test_mildnonaxi_meanvt_grid_rmEstimates():
    # Test vmomentsurfacemass w/o having the _estimateX functions in the intial DF
    class fakeDehnen(dehnendf):  #class that removes the _estimate functions
        def __init__(self, *args, **kwargs):
            dehnendf.__init__(self, *args, **kwargs)

        _estimatemeanvR = property()
        _estimatemeanvT = property()
        _estimateSigmaR2 = property()
        _estimateSigmaT2 = property()

    idf = fakeDehnen(beta=0.)
    pot = [
        LogarithmicHaloPotential(normalize=1.),
        SteadyLogSpiralPotential(A=-0.005, omegas=0.2)
    ]  #very mild non-axi
    edf = evolveddiskdf(idf, pot=pot, to=-10.)
    mvt, grid = edf.meanvT(0.9,
                           phi=0.2,
                           integrate_method='rk6_c',
                           grid=True,
                           returnGrid=True,
                           gridpoints=_GRIDPOINTS)
    assert numpy.fabs(
        mvt - idf.meanvT(0.9)
    ) < 0.005, 'meanvT of evolveddiskdf for axisymmetric potential is not equal to that of the initial dehnendf'
    return None
Exemplo n.º 10
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def test_plot_hierarchgrid():
    idf = dehnendf(beta=0.)
    pot = [
        LogarithmicHaloPotential(normalize=1.),
        SteadyLogSpiralPotential(A=-0.005, omegas=0.2)
    ]  #very mild non-axi
    edf = evolveddiskdf(idf, pot=pot, to=-10.)
    mvr, grid = edf.meanvR(0.9,
                           phi=0.2,
                           integrate_method='rk6_c',
                           grid=True,
                           hierarchgrid=True,
                           returnGrid=True,
                           gridpoints=_GRIDPOINTS)
    grid.plot()
    #w/ list of tiems
    mvr, grid = edf.meanvR(0.9,
                           t=[0., -2.5, -5., -7.5, -10.],
                           phi=0.2,
                           integrate_method='rk6_c',
                           grid=True,
                           hierarchgrid=True,
                           returnGrid=True,
                           gridpoints=_GRIDPOINTS)
    grid.plot(1)
    return None
Exemplo n.º 11
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def test_mildnonaxi_meanvt_hierarchgrid_tlist():
    # Test that for a close to axisymmetric potential, the mean vt is close to that of the initial DF
    idf = dehnendf(beta=0.)
    pot = [
        LogarithmicHaloPotential(normalize=1.),
        SteadyLogSpiralPotential(A=-0.005, omegas=0.2)
    ]  #very mild non-axi
    edf = evolveddiskdf(idf, pot=pot, to=-10.)
    mvt, grid = edf.meanvT(0.9,
                           t=[0., -2.5, -5., -7.5, -10.],
                           phi=0.2,
                           integrate_method='rk6_c',
                           grid=True,
                           hierarchgrid=True,
                           returnGrid=True,
                           gridpoints=_GRIDPOINTS)
    assert numpy.all(
        numpy.fabs(mvt - idf.meanvT(0.9)) < 0.005
    ), 'meanvT of evolveddiskdf for axisymmetric potential is not equal to that of the initial dehnendf when using hierarchgrid and tlist'
    mvt = edf.meanvT(0.9,
                     t=[0., -2.5, -5., -7.5, -10.],
                     phi=0.2,
                     integrate_method='rk6_c',
                     grid=grid,
                     gridpoints=_GRIDPOINTS)
    assert numpy.all(
        numpy.fabs(mvt - idf.meanvT(0.9)) < 0.005
    ), 'meanvT of evolveddiskdf for axisymmetric potential is not equal to that of the initial dehnendf when calculated with pre-computed grid when using hierarchgrid and tlist'
    return None
Exemplo n.º 12
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def test_mildnonaxi_meanvt_hierarchgrid():
    # Test that for a close to axisymmetric potential, the mean vt is close to that of the initial DF
    idf= dehnendf(beta=0.)
    pot= [LogarithmicHaloPotential(normalize=1.),
          SteadyLogSpiralPotential(A=-0.005,omegas=0.2)] #very mild non-axi
    edf= evolveddiskdf(idf,pot=pot,to=-10.)
    mvt, grid= edf.meanvT(0.9,phi=0.2,integrate_method='rk6_c',
                          grid=True,hierarchgrid=True,
                          returnGrid=True,gridpoints=_GRIDPOINTS)
    assert numpy.fabs(mvt-idf.meanvT(0.9)) < 0.005, 'meanvT of evolveddiskdf for axisymmetric potential is not equal to that of the initial dehnendf when using hierarchgrid'
    mvt= edf.meanvT(0.9,phi=0.2,integrate_method='rk6_c',grid=grid,
                    gridpoints=_GRIDPOINTS)
    assert numpy.fabs(mvt-idf.meanvT(0.9)) < 0.005, 'meanvT of evolveddiskdf for axisymmetric potential is not equal to that of the initial dehnendf when calculated with pre-computed grid when using hierarchgrid'
    #Also test that the hierarchgrid is properly returned
    smass, ngrid= edf.vmomentsurfacemass(0.9,0,0,phi=0.2,
                                        integrate_method='rk6_c',
                                        grid=grid,gridpoints=_GRIDPOINTS,
                                         returnGrid=True)
    assert ngrid == grid, 'hierarchical grid returned by vmomentsurfacemass w/ grid input is not the same as the input'
    nsmass= edf.vmomentsurfacemass(0.9,0,0,phi=0.2,
                                   integrate_method='rk6_c',
                                   grid=True,hierarchgrid=True,
                                   gridpoints=_GRIDPOINTS)
    assert numpy.fabs(smass-nsmass) < 0.001, 'surfacemass computed w/ and w/o returnGrid are not the same'
    return None
Exemplo n.º 13
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def test_mildnonaxi_meanvr_grid():
    # Test that for a close to axisymmetric potential, the mean vr is close to zero
    idf= dehnendf(beta=0.)
    pot= [LogarithmicHaloPotential(normalize=1.),
          SteadyLogSpiralPotential(A=-0.005,omegas=0.2)] #very mild non-axi
    edf= evolveddiskdf(idf,pot=pot,to=-10.)
    mvr, grid= edf.meanvR(0.9,phi=0.2,integrate_method='rk6_c',grid=True,
                          returnGrid=True,gridpoints=_GRIDPOINTS)
    assert numpy.fabs(mvr) < 0.001, 'meanvR of evolveddiskdf for axisymmetric potential is not equal to zero'
    mvr= edf.meanvR(0.9,phi=0.2,integrate_method='rk6_c',grid=grid)
    assert numpy.fabs(mvr) < 0.001, 'meanvR of evolveddiskdf for axisymmetric potential is not equal to zero when calculated with pre-computed grid'
    #Pre-compute surfmass and use it, first test that grid is properly returned when given
    smass, ngrid= edf.vmomentsurfacemass(0.9,0,0,phi=0.2,
                                        integrate_method='rk6_c',
                                        grid=grid,gridpoints=_GRIDPOINTS,
                                        returnGrid=True)
    assert ngrid == grid, 'grid returned by vmomentsurfacemass w/ grid input is not the same as the input'
    #Pre-compute surfmass and use it
    nsmass= edf.vmomentsurfacemass(0.9,0,0,phi=0.2,
                                   integrate_method='rk6_c',
                                   grid=True,gridpoints=_GRIDPOINTS)
    assert numpy.fabs(smass-nsmass) < 0.001, 'surfacemass computed w/ and w/o returnGrid are not the same'
    mvr= edf.meanvR(0.9,phi=0.2,integrate_method='rk6_c',grid=grid,
                    surfacemass=smass)
    assert numpy.fabs(mvr) < 0.001, 'meanvR of evolveddiskdf for axisymmetric potential is not equal to zero when calculated with pre-computed grid and surfacemass'
    global _maxi_meanvr
    _maxi_meanvr= mvr
    global _maxi_surfacemass
    _maxi_surfacemass= smass
    return None
Exemplo n.º 14
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def test_elliptical_cold_vt():
    # Test that the rotational velocity for the elliptical disk behaves as analytically expected
    idf = dehnendf(beta=0., profileParams=(1. / 3., 1., 0.0125))
    cp = 0.05
    pot = [
        LogarithmicHaloPotential(normalize=1.),
        EllipticalDiskPotential(cp=cp, sp=0., p=0., tform=-150., tsteady=125.)
    ]
    edf = evolveddiskdf(idf, pot=pot, to=-150.)
    #Should be 1.
    mvt, grid = edf.meanvT(0.9,
                           phi=-numpy.pi / 4.,
                           integrate_method='rk6_c',
                           grid=True,
                           returnGrid=True,
                           gridpoints=_GRIDPOINTS)
    assert numpy.fabs(
        mvt - 1.
    ) < 10.**-3., 'Cold elliptical disk does not agree with analytical calculation for vt'
    #Should be 1.-cp
    mvt, grid = edf.meanvT(0.9,
                           phi=0.,
                           integrate_method='rk6_c',
                           grid=True,
                           nsigma=7.,
                           returnGrid=True,
                           gridpoints=_GRIDPOINTS)
    assert numpy.fabs(
        mvt - 1. + cp
    ) < 10.**-3., 'Cold elliptical disk does not agree with analytical calculation for vt'
    return None
Exemplo n.º 15
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def test_elliptical_cold_oortABCK_position2():
    # Test that the Oort functions A, B, C, and K for the elliptical disk behaves as analytically expected
    idf= dehnendf(beta=0.,profileParams=(1./3.,1.,0.0125))
    cp= 0.05
    pot= [LogarithmicHaloPotential(normalize=1.),
          EllipticalDiskPotential(cp=cp,sp=0.,p=0.,tform=-150.,tsteady=125.)]
    edf= evolveddiskdf(idf,pot=pot,to=-150.)
    #Should be 0.5/0.9+cp/2
    oorta, grid, gridr, gridp= edf.oortA(0.9,phi=0.,
                                         integrate_method='rk6_c',grid=True,
                                         nsigma=7.,
                                         derivRGrid=True,derivphiGrid=True,
                                         returnGrids=True,
                                         gridpoints=51,
                                         derivGridpoints=51)
    assert numpy.fabs(oorta-cp/2.-0.5/0.9) < 10.**-2.2, 'Cold elliptical disk does not agree with analytical calculation for oortA'
    #Should be -cp/2-0.5/0.9
    oortb= edf.oortB(0.9,phi=0.,
                     integrate_method='rk6_c',grid=grid,nsigma=7.,
                     derivRGrid=gridr,derivphiGrid=gridp)
    assert numpy.fabs(oortb+cp/2.+0.5/0.9) < 10.**-2.2, 'Cold elliptical disk does not agree with analytical calculation for oortB'
    #Should be 0
    oortc= edf.oortC(0.9,phi=0.,
                     integrate_method='rk6_c',grid=grid,nsigma=7.,
                     derivRGrid=gridr,derivphiGrid=gridp)
    assert numpy.fabs(oortc) < 10.**-3., 'Cold elliptical disk does not agree with analytical calculation for oortC'
    #Should be 0
    oortk= edf.oortK(0.9,phi=0.,
                     integrate_method='rk6_c',grid=grid,nsigma=7.,
                     derivRGrid=gridr,derivphiGrid=gridp)
    assert numpy.fabs(oortk) < 10.**-3., 'Cold elliptical disk does not agree with analytical calculation for oortK'
    return None
Exemplo n.º 16
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def test_mildnonaxi_vertexdev_direct():
    # Test that for an axisymmetric potential, the vertex deviation is close zero
    # We do this for an axisymmetric potential, bc otherwise it takes too long
    idf= dehnendf(beta=0.)
    pot= [LogarithmicHaloPotential(normalize=1.)]
    edf= evolveddiskdf(idf,pot=pot,to=-10.)
    vdev= edf.vertexdev(0.9,phi=0.2,integrate_method='rk6_c',grid=False)
    assert numpy.fabs(vdev) < 0.01/180.*numpy.pi, 'vertexdev of evolveddiskdf for axisymmetric potential is not equal to zero when calculated directly'
    return None
Exemplo n.º 17
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def test_mildnonaxi_meanvt_direct():
    # Test that for a close to axisymmetric potential, the mean vt is close to that of the initial DF
    # We do this for an axisymmetric potential, bc otherwise it takes too long
    idf= dehnendf(beta=0.)
    pot= [LogarithmicHaloPotential(normalize=1.)]
    edf= evolveddiskdf(idf,pot=pot,to=-10.)
    mvt= edf.meanvT(0.9,phi=0.2,integrate_method='rk6_c',grid=False)
    assert numpy.fabs(mvt-idf.meanvT(0.9)) < 0.001, 'meanvT of evolveddiskdf for axisymmetric potential is not equal to that of the initial dehnendf when using direct integration'
    return None
Exemplo n.º 18
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def test_mildnonaxi_meanvt_direct():
    # Test that for a close to axisymmetric potential, the mean vt is close to that of the initial DF
    # We do this for an axisymmetric potential, bc otherwise it takes too long
    idf= dehnendf(beta=0.)
    pot= [LogarithmicHaloPotential(normalize=1.)]
    edf= evolveddiskdf(idf,pot=pot,to=-10.)
    mvt= edf.meanvT(0.9,phi=0.2,integrate_method='rk6_c',grid=False)
    assert numpy.fabs(mvt-idf.meanvT(0.9)) < 0.001, 'meanvT of evolveddiskdf for axisymmetric potential is not equal to that of the initial dehnendf when using direct integration'
    return None
Exemplo n.º 19
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def test_mildnonaxi_meanvr_direct():
    # Test that for an axisymmetric potential, the mean vr is close to zero
    # We do this for an axisymmetric potential, bc otherwise it takes too long
    idf= dehnendf(beta=0.)
    pot= [LogarithmicHaloPotential(normalize=1.)]
    edf= evolveddiskdf(idf,pot=pot,to=-10.)
    mvr= edf.meanvR(0.9,phi=0.2,integrate_method='rk6_c',grid=False)
    assert numpy.fabs(mvr) < 0.001, 'meanvR of evolveddiskdf for axisymmetric potential is not equal to zero when calculated directly'
    return None
Exemplo n.º 20
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def test_mildnonaxi_meanvr_direct():
    # Test that for an axisymmetric potential, the mean vr is close to zero
    # We do this for an axisymmetric potential, bc otherwise it takes too long
    idf= dehnendf(beta=0.)
    pot= [LogarithmicHaloPotential(normalize=1.)]
    edf= evolveddiskdf(idf,pot=pot,to=-10.)
    mvr= edf.meanvR(0.9,phi=0.2,integrate_method='rk6_c',grid=False)
    assert numpy.fabs(mvr) < 0.001, 'meanvR of evolveddiskdf for axisymmetric potential is not equal to zero when calculated directly'
    return None
Exemplo n.º 21
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def test_mildnonaxi_sigmat2_direct():
    # Test that for an axisymmetric potential, the sigmaT2  is close to the value of the initial DF   
    # We do this for an axisymmetric potential, bc otherwise it takes too long
    idf= dehnendf(beta=0.)
    pot= [LogarithmicHaloPotential(normalize=1.)]
    edf= evolveddiskdf(idf,pot=pot,to=-10.)
    st2= edf.sigmaT2(0.9,phi=0.2,integrate_method='rk6_c',grid=False)
    ist2= idf.sigmaT2(0.9)
    assert numpy.fabs(numpy.log(st2)-numpy.log(ist2)) < 0.025, 'sigmat2 of evolveddiskdf for axisymmetric potential is not equal to that of initial DF when calculated directly'
    return None
Exemplo n.º 22
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def test_mildnonaxi_sigmat2_direct():
    # Test that for an axisymmetric potential, the sigmaT2  is close to the value of the initial DF   
    # We do this for an axisymmetric potential, bc otherwise it takes too long
    idf= dehnendf(beta=0.)
    pot= [LogarithmicHaloPotential(normalize=1.)]
    edf= evolveddiskdf(idf,pot=pot,to=-10.)
    st2= edf.sigmaT2(0.9,phi=0.2,integrate_method='rk6_c',grid=False)
    ist2= idf.sigmaT2(0.9)
    assert numpy.fabs(numpy.log(st2)-numpy.log(ist2)) < 0.025, 'sigmat2 of evolveddiskdf for axisymmetric potential is not equal to that of initial DF when calculated directly'
    return None
Exemplo n.º 23
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def test_mildnonaxi_meanvt_direct_tlist():
    # Shouldn't work
    idf= dehnendf(beta=0.)
    pot= [LogarithmicHaloPotential(normalize=1.)]
    edf= evolveddiskdf(idf,pot=pot,to=-10.)
    try:
        edf.meanvT(0.9,t=[0.,-2.5,-5.,-7.5,-10.],
                   phi=0.2,integrate_method='rk6_c',grid=False)
    except IOError: pass
    else: raise AssertionError('direct evolveddiskdf calculation of meanvT w/ list of times did not raise IOError')
    return None
Exemplo n.º 24
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def test_mildnonaxi_vertexdev_direct():
    # Test that for an axisymmetric potential, the vertex deviation is close zero
    # We do this for an axisymmetric potential, bc otherwise it takes too long
    idf = dehnendf(beta=0.)
    pot = [LogarithmicHaloPotential(normalize=1.)]
    edf = evolveddiskdf(idf, pot=pot, to=-10.)
    vdev = edf.vertexdev(0.9, phi=0.2, integrate_method='rk6_c', grid=False)
    assert numpy.fabs(
        vdev
    ) < 0.01 / 180. * numpy.pi, 'vertexdev of evolveddiskdf for axisymmetric potential is not equal to zero when calculated directly'
    return None
Exemplo n.º 25
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def test_mildnonaxi_meanvt_direct_tlist():
    # Shouldn't work
    idf= dehnendf(beta=0.)
    pot= [LogarithmicHaloPotential(normalize=1.)]
    edf= evolveddiskdf(idf,pot=pot,to=-10.)
    try:
        edf.meanvT(0.9,t=[0.,-2.5,-5.,-7.5,-10.],
                   phi=0.2,integrate_method='rk6_c',grid=False)
    except IOError: pass
    else: raise AssertionError('direct evolveddiskdf calculation of meanvT w/ list of times did not raise IOError')
    return None
Exemplo n.º 26
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def test_call_special():
    from galpy.orbit import Orbit
    idf= dehnendf(beta=0.)
    pot= [LogarithmicHaloPotential(normalize=1.),
          EllipticalDiskPotential(twophio=0.001)] #very mild non-axi
    edf= evolveddiskdf(idf,pot=pot,to=-10.)
    o= Orbit([0.9,0.1,1.1,2.])
    #call w/ and w/o explicit t
    assert numpy.fabs(numpy.log(edf(o,0.))-numpy.log(edf(o))) < 10.**-10., 'edf.__call__ w/ explicit t=0. and w/o t do not give the same answer'
    #call must get Orbit, otherwise error
    try: edf(0.9,0.1,1.1,2.)
    except IOError: pass
    else: raise AssertionError('edf.__call__ w/o Orbit input did not raise IOError')
    #Call w/ list, but just to
    assert numpy.fabs(numpy.log(edf(o,[-10.]))-numpy.log(idf(o))) < 10.**-10., 'edf.__call__ w/ tlist set to [to] did not return initial DF'
    #Call w/ just to
    assert numpy.fabs(numpy.log(edf(o,-10.))-numpy.log(idf(o))) < 10.**-10., 'edf.__call__ w/ tlist set to [to] did not return initial DF'
    #also w/ log
    assert numpy.fabs(edf(o,[-10.],log=True)-numpy.log(idf(o))) < 10.**-10., 'edf.__call__ w/ tlist set to [to] did not return initial DF (log)'
    assert numpy.fabs(edf(o,-10.,log=True)-numpy.log(idf(o))) < 10.**-10., 'edf.__call__ w/ tlist set to [to] did not return initial DF (log)'
    # Tests w/ odeint: tlist
    codeint= edf(o,[0.,-2.5,-5.,-7.5,-10.],integrate_method='odeint',log=True)
    crk6c= edf(o,[0.,-2.5,-5.,-7.5,-10.],integrate_method='rk6_c',log=True)
    assert numpy.all(numpy.fabs(codeint-crk6c) < 10.**-4.), 'edf.__call__ w/ odeint and tlist does not give the same result as w/ rk6_c'
    # Crazy orbit w/ tlist
    crk6c= edf(Orbit([3.,1.,-1.,2.]),[0.],integrate_method='odeint',log=True)
    assert crk6c < -20., 'crazy orbit does not have DF equal to zero'
    # deriv w/ odeint
    codeint= edf(o,[0.,-2.5,-5.,-7.5,-10.],integrate_method='odeint',
                 deriv='R')
    crk6c= edf(o,[0.,-2.5,-5.,-7.5,-10.],integrate_method='rk6_c',deriv='R')
    assert numpy.all(numpy.fabs(codeint-crk6c) < 10.**-4.), 'edf.__call__ w/ odeint and tlist does not give the same result as w/ rk6_c (deriv=R)'
    # deriv w/ len(tlist)=1
    crk6c= edf(o,[0.],integrate_method='rk6_c',deriv='R')
    crk6c2= edf(o,0.,integrate_method='rk6_c',deriv='R')
    assert numpy.all(numpy.fabs(crk6c-crk6c2) < 10.**-4.), 'edf.__call__ w/ tlist consisting of one time and just a scalar time do not agree'
    #Call w/ just to and deriv
    assert numpy.fabs(edf(o,-10.,deriv='R')-idf(o)*idf._dlnfdR(o._orb.vxvv[0],o._orb.vxvv[1],o._orb.vxvv[2])) < 10.**-10., 'edf.__call__ w/ to did not return initial DF (deriv=R)'
    assert numpy.fabs(edf(o,-10.,deriv='phi')) < 10.**-10., 'edf.__call__ w/ to did not return initial DF (deriv=phi)'
    # Call w/ just one t and odeint
    codeint= edf(o,0,integrate_method='odeint',log=True)
    crk6c= edf(o,0.,integrate_method='rk6_c',log=True)
    assert numpy.fabs(codeint-crk6c) < 10.**-4., 'edf.__call__ w/ odeint and tlist does not give the same result as w/ rk6_c'
    # Call w/ just one t and fallback to odeint
    # turn off C
    edf._pot[0].hasC= False
    edf._pot[0].hasC_dxdv= False
    codeint= edf(o,0,integrate_method='dopr54_c',log=True)
    assert numpy.fabs(codeint-crk6c) < 10.**-4., 'edf.__call__ w/ odeint and tlist does not give the same result as w/ rk6_c'
    # Call w/ just one t and fallback to leaprog
    cleapfrog= edf(o,0,integrate_method='leapfrog_c',log=True)
    assert numpy.fabs(cleapfrog-crk6c) < 10.**-4., 'edf.__call__ w/ odeint and tlist does not give the same result as w/ rk6_c'
Exemplo n.º 27
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def test_call_marginalizevperp():
    from galpy.orbit import Orbit
    idf= dehnendf(beta=0.)
    pot= [LogarithmicHaloPotential(normalize=1.),
          EllipticalDiskPotential(twophio=0.001)] #very mild non-axi
    edf= evolveddiskdf(idf,pot=pot[0],to=-10.) #one with just one potential
    #l=0
    R,phi,vR = 0.8, 0., 0.4
    vts= numpy.linspace(0.,1.5,51)
    pvts= numpy.array([edf(Orbit([R,vR,vt,phi]),integrate_method='rk6_c') for vt in vts])
    assert numpy.fabs(numpy.sum(pvts)*(vts[1]-vts[0])\
                          -edf(Orbit([R,vR,0.,phi]),marginalizeVperp=True,integrate_method='rk6_c')) < 10.**-3.5, 'evolveddiskdf call w/ marginalizeVperp does not work'
    #l=270
    edf= evolveddiskdf(idf,pot=pot,to=-10.)
    R,phi,vT = numpy.sin(numpy.pi/6.), -numpy.pi/3.,0.7 #l=30 degree
    vrs= numpy.linspace(-1.,1.,101)
    pvrs= numpy.array([edf(Orbit([R,vr,vT,phi]),integrate_method='rk6_c') for vr in vrs])
    assert numpy.fabs(numpy.log(numpy.sum(pvrs)*(vrs[1]-vrs[0]))\
                                    -edf(Orbit([R,0.,vT,phi]),
                                         marginalizeVperp=True,
                                         integrate_method='rk6_c',log=True,
                                         nsigma=4)) < 10.**-2.5, 'evolveddiskdf call w/ marginalizeVperp does not work'
    return None
Exemplo n.º 28
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def test_call_marginalizevlos():
    from galpy.orbit import Orbit
    idf= dehnendf(beta=0.)
    pot= [LogarithmicHaloPotential(normalize=1.),
          EllipticalDiskPotential(twophio=0.001)] #very mild non-axi
    edf= evolveddiskdf(idf,pot=pot[0],to=-10.) #one with just one potential
    #l=0
    R,phi,vT = 0.8, 0., 0.7
    vrs= numpy.linspace(-1.,1.,101)
    pvrs= numpy.array([edf(Orbit([R,vr,vT,phi]),integrate_method='rk6_c') for vr in vrs])
    assert numpy.fabs(numpy.log(numpy.sum(pvrs)*(vrs[1]-vrs[0]))\
                          -edf(Orbit([R,0.,vT,phi]),marginalizeVlos=True,integrate_method='rk6_c',log=True)) < 10.**-4., 'diskdf call w/ marginalizeVlos does not work'
    #l=270, this DF has some issues, but it suffices to test the mechanics of the code
    edf= evolveddiskdf(idf,pot=pot,to=-10.)
    R,phi,vR = numpy.sin(numpy.pi/6.), -numpy.pi/3.,0.4 #l=30 degree
    vts= numpy.linspace(0.3,1.5,101)
    pvts= numpy.array([edf(Orbit([R,vR,vt,phi]),integrate_method='rk6_c') for vt in vts])
    assert numpy.fabs(numpy.sum(pvts)*(vts[1]-vts[0])\
                          -edf(Orbit([R,vR,0.,phi]),
                               marginalizeVlos=True,
                               integrate_method='rk6_c',
                               nsigma=4)) < 10.**-3.5, 'diskdf call w/ marginalizeVlos does not work'
    return None
Exemplo n.º 29
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def test_plot_grid():
    idf= dehnendf(beta=0.)
    pot= [LogarithmicHaloPotential(normalize=1.),
          SteadyLogSpiralPotential(A=-0.005,omegas=0.2)] #very mild non-axi
    edf= evolveddiskdf(idf,pot=pot,to=-10.)
    mvr, grid= edf.meanvR(0.9,phi=0.2,integrate_method='rk6_c',grid=True,
                          returnGrid=True,gridpoints=_GRIDPOINTS)
    grid.plot()
    #w/ list of tiems
    mvr, grid= edf.meanvR(0.9,t=[0.,-2.5,-5.,-7.5,-10.],
                          phi=0.2,integrate_method='rk6_c',grid=True,
                          returnGrid=True,gridpoints=_GRIDPOINTS)
    grid.plot(1)
    return None
Exemplo n.º 30
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def test_call_marginalizevperp():
    from galpy.orbit import Orbit
    idf= dehnendf(beta=0.)
    pot= [LogarithmicHaloPotential(normalize=1.),
          EllipticalDiskPotential(twophio=0.001)] #very mild non-axi
    edf= evolveddiskdf(idf,pot=pot[0],to=-10.) #one with just one potential
    #l=0
    R,phi,vR = 0.8, 0., 0.4
    vts= numpy.linspace(0.,1.5,51)
    pvts= numpy.array([edf(Orbit([R,vR,vt,phi]),integrate_method='rk6_c') for vt in vts])
    assert numpy.fabs(numpy.sum(pvts)*(vts[1]-vts[0])\
                          -edf(Orbit([R,vR,0.,phi]),marginalizeVperp=True,integrate_method='rk6_c')) < 10.**-3.5, 'evolveddiskdf call w/ marginalizeVperp does not work'
    #l=270
    edf= evolveddiskdf(idf,pot=pot,to=-10.)
    R,phi,vT = numpy.sin(numpy.pi/6.), -numpy.pi/3.,0.7 #l=30 degree
    vrs= numpy.linspace(-1.,1.,101)
    pvrs= numpy.array([edf(Orbit([R,vr,vT,phi]),integrate_method='rk6_c') for vr in vrs])
    assert numpy.fabs(numpy.log(numpy.sum(pvrs)*(vrs[1]-vrs[0]))\
                                    -edf(Orbit([R,0.,vT,phi]),
                                         marginalizeVperp=True,
                                         integrate_method='rk6_c',log=True,
                                         nsigma=4)) < 10.**-2.5, 'evolveddiskdf call w/ marginalizeVperp does not work'
    return None
Exemplo n.º 31
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def test_call_marginalizevlos():
    from galpy.orbit import Orbit
    idf= dehnendf(beta=0.)
    pot= [LogarithmicHaloPotential(normalize=1.),
          EllipticalDiskPotential(twophio=0.001)] #very mild non-axi
    edf= evolveddiskdf(idf,pot=pot[0],to=-10.) #one with just one potential
    #l=0
    R,phi,vT = 0.8, 0., 0.7
    vrs= numpy.linspace(-1.,1.,101)
    pvrs= numpy.array([edf(Orbit([R,vr,vT,phi]),integrate_method='rk6_c') for vr in vrs])
    assert numpy.fabs(numpy.log(numpy.sum(pvrs)*(vrs[1]-vrs[0]))\
                          -edf(Orbit([R,0.,vT,phi]),marginalizeVlos=True,integrate_method='rk6_c',log=True)) < 10.**-4., 'diskdf call w/ marginalizeVlos does not work'
    #l=270, this DF has some issues, but it suffices to test the mechanics of the code
    edf= evolveddiskdf(idf,pot=pot,to=-10.)
    R,phi,vR = numpy.sin(numpy.pi/6.), -numpy.pi/3.,0.4 #l=30 degree
    vts= numpy.linspace(0.3,1.5,101)
    pvts= numpy.array([edf(Orbit([R,vR,vt,phi]),integrate_method='rk6_c') for vt in vts])
    assert numpy.fabs(numpy.sum(pvts)*(vts[1]-vts[0])\
                          -edf(Orbit([R,vR,0.,phi]),
                               marginalizeVlos=True,
                               integrate_method='rk6_c',
                               nsigma=4)) < 10.**-3.5, 'diskdf call w/ marginalizeVlos does not work'
    return None
Exemplo n.º 32
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def test_mildnonaxi_meanvr_grid_tlist():
    # Test that for a close to axisymmetric potential, the mean vr is close to zero
    idf= dehnendf(beta=0.)
    pot= [LogarithmicHaloPotential(normalize=1.),
          SteadyLogSpiralPotential(A=-0.005,omegas=0.2)] #very mild non-axi
    edf= evolveddiskdf(idf,pot=pot,to=-10.)
    mvr, grid= edf.meanvR(0.9,t=[0.,-2.5,-5.,-7.5,-10.],
                          phi=0.2,integrate_method='rk6_c',
                          grid=True,returnGrid=True,gridpoints=_GRIDPOINTS)
    assert numpy.all(numpy.fabs(mvr) < 0.003), 'meanvR of evolveddiskdf for axisymmetric potential is not equal to zero for list of times'
    mvr= edf.meanvR(0.9,t=[0.,-2.5,-5.,-7.5,-10.],
                    phi=0.2,integrate_method='rk6_c',grid=grid)
    assert numpy.all(numpy.fabs(mvr) < 0.003), 'meanvR of evolveddiskdf for axisymmetric potential is not equal to zero when calculated with pre-computed grid for list of times'
    return None
Exemplo n.º 33
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def test_mildnonaxi_meanvt_grid():
    # Test that for a close to axisymmetric potential, the mean vt is close to that of the initial DF
    idf= dehnendf(beta=0.)
    pot= [LogarithmicHaloPotential(normalize=1.),
          SteadyLogSpiralPotential(A=-0.005,omegas=0.2)] #very mild non-axi
    edf= evolveddiskdf(idf,pot=pot,to=-10.)
    mvt, grid= edf.meanvT(0.9,phi=0.2,integrate_method='rk6_c',grid=True,
                          returnGrid=True,gridpoints=_GRIDPOINTS)
    assert numpy.fabs(mvt-idf.meanvT(0.9)) < 0.005, 'meanvT of evolveddiskdf for axisymmetric potential is not equal to that of the initial dehnendf'
    mvt= edf.meanvT(0.9,phi=0.2,integrate_method='rk6_c',grid=grid,
                    gridpoints=_GRIDPOINTS,)
    assert numpy.fabs(mvt-idf.meanvT(0.9)) < 0.005, 'meanvT of evolveddiskdf for axisymmetric potential is not equal to that of the initial dehnendf when calculated with pre-computed grid'
    global _maxi_meanvt
    _maxi_meanvt= mvt
    return None
Exemplo n.º 34
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def test_mildnonaxi_vertexdev_grid():
    # Test that for a close to axisymmetric potential, the vertex deviation is close to zero
    idf= dehnendf(beta=0.)
    pot= [LogarithmicHaloPotential(normalize=1.),
          SteadyLogSpiralPotential(A=-0.005,omegas=0.2)] #very mild non-axi
    edf= evolveddiskdf(idf,pot=pot,to=-10.)
    vdev, grid= edf.vertexdev(0.9,phi=0.2,integrate_method='rk6_c',grid=True,
                              returnGrid=True,gridpoints=_GRIDPOINTS)
    assert numpy.fabs(vdev) < 2., 'vertexdev of evolveddiskdf for axisymmetric potential is not close to zero' #2 is pretty big, but the weak spiral creates that
    vdev= edf.vertexdev(0.9,phi=0.2,integrate_method='rk6_c',grid=grid,
                        gridpoints=_GRIDPOINTS)
    assert numpy.fabs(vdev) < 2., 'vertexdev of evolveddiskdf for axisymmetric potential is not equal zero when calculated with pre-computed grid'
    vdev= edf.vertexdev(0.9,phi=0.2,integrate_method='rk6_c',grid=grid,
                        sigmaR2=_maxi_sigmar2,sigmaT2=_maxi_sigmat2,
                        sigmaRT=_maxi_sigmart,gridpoints=_GRIDPOINTS)
    assert numpy.fabs(vdev) < 2., 'sigmart of evolveddiskdf for axisymmetric potential is not equal to zero when calculated with pre-computed sigmaR2,sigmaT2,sigmaRT'
    return None
Exemplo n.º 35
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def test_mildnonaxi_oortK_grid():
    # Test that for a close to axisymmetric potential, the oortK is close to zero
    idf= dehnendf(beta=0.)
    pot= [LogarithmicHaloPotential(normalize=1.),
          EllipticalDiskPotential(twophio=0.001)] #very mild non-axi
    edf= evolveddiskdf(idf,pot=pot,to=-10.)
    ok, grid, dgridR, dgridphi=\
        edf.oortK(0.9,phi=0.2,integrate_method='rk6_c',
                  grid=True,derivRGrid=True,derivphiGrid=True,
                  returnGrids=True,
                  gridpoints=_GRIDPOINTS,derivGridpoints=_GRIDPOINTS)
    assert numpy.fabs(ok) < 0.005, 'oortK of evolveddiskdf for axisymmetric potential is not equal to that of initial DF'
    ok= edf.oortK(0.9,phi=0.2,integrate_method='rk6_c',grid=grid,
                    derivRGrid=dgridR,derivphiGrid=dgridphi,
                  gridpoints=_GRIDPOINTS,derivGridpoints=_GRIDPOINTS)
    assert numpy.fabs(ok) < 0.005, 'oortK of evolveddiskdf for axisymmetric potential is not equal to that of initial DF when calculated with pre-computed grid'
    return None
Exemplo n.º 36
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def test_mildnonaxi_vertexdev_grid():
    # Test that for a close to axisymmetric potential, the vertex deviation is close to zero
    idf= dehnendf(beta=0.)
    pot= [LogarithmicHaloPotential(normalize=1.),
          SteadyLogSpiralPotential(A=-0.005,omegas=0.2)] #very mild non-axi
    edf= evolveddiskdf(idf,pot=pot,to=-10.)
    vdev, grid= edf.vertexdev(0.9,phi=0.2,integrate_method='rk6_c',grid=True,
                              returnGrid=True,gridpoints=_GRIDPOINTS)
    assert numpy.fabs(vdev) < 2., 'vertexdev of evolveddiskdf for axisymmetric potential is not close to zero' #2 is pretty big, but the weak spiral creates that
    vdev= edf.vertexdev(0.9,phi=0.2,integrate_method='rk6_c',grid=grid,
                        gridpoints=_GRIDPOINTS)
    assert numpy.fabs(vdev) < 2., 'vertexdev of evolveddiskdf for axisymmetric potential is not equal zero when calculated with pre-computed grid'
    vdev= edf.vertexdev(0.9,phi=0.2,integrate_method='rk6_c',grid=grid,
                        sigmaR2=_maxi_sigmar2,sigmaT2=_maxi_sigmat2,
                        sigmaRT=_maxi_sigmart,gridpoints=_GRIDPOINTS)
    assert numpy.fabs(vdev) < 2., 'sigmart of evolveddiskdf for axisymmetric potential is not equal to zero when calculated with pre-computed sigmaR2,sigmaT2,sigmaRT'
    return None
Exemplo n.º 37
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def test_mildnonaxi_meanvt_grid_rmEstimates():
    # Test vmomentsurfacemass w/o having the _estimateX functions in the intial DF
    class fakeDehnen(dehnendf): #class that removes the _estimate functions
        def __init__(self,*args,**kwargs):
            dehnendf.__init__(self,*args,**kwargs)
        _estimatemeanvR= property()
        _estimatemeanvT= property()
        _estimateSigmaR2= property()
        _estimateSigmaT2= property()
    idf= fakeDehnen(beta=0.)
    pot= [LogarithmicHaloPotential(normalize=1.),
          SteadyLogSpiralPotential(A=-0.005,omegas=0.2)] #very mild non-axi
    edf= evolveddiskdf(idf,pot=pot,to=-10.)
    mvt, grid= edf.meanvT(0.9,phi=0.2,integrate_method='rk6_c',grid=True,
                          returnGrid=True,gridpoints=_GRIDPOINTS)
    assert numpy.fabs(mvt-idf.meanvT(0.9)) < 0.005, 'meanvT of evolveddiskdf for axisymmetric potential is not equal to that of the initial dehnendf'
    return None
Exemplo n.º 38
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def test_elliptical_cold_vertexdev():
    # Test that the vertex deviations for the elliptical disk behaves as analytically expected
    idf= dehnendf(beta=0.,profileParams=(1./3.,1.,0.0125))
    cp= 0.05
    pot= [LogarithmicHaloPotential(normalize=1.),
          EllipticalDiskPotential(cp=cp,sp=0.,p=0.,tform=-150.,tsteady=125.)]
    edf= evolveddiskdf(idf,pot=pot,to=-150.)
    #Should be -2cp in radians
    vdev, grid= edf.vertexdev(0.9,phi=-numpy.pi/4.,
                              integrate_method='rk6_c',grid=True,nsigma=7.,
                              returnGrid=True,gridpoints=_GRIDPOINTS)
    assert numpy.fabs(vdev/180.*numpy.pi+2.*cp) < 10.**-3., 'Cold elliptical disk does not agree with analytical calculation for vertexdev'
    #Should be 0
    vdev, grid= edf.vertexdev(0.9,phi=0.,
                              integrate_method='rk6_c',grid=True,nsigma=7.,
                              returnGrid=True,gridpoints=_GRIDPOINTS)
    assert numpy.fabs(vdev) < 10.**-2., 'Cold elliptical disk does not agree with analytical calculation for vertexdev'
    return None
Exemplo n.º 39
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def test_elliptical_cold_vt():
    # Test that the rotational velocity for the elliptical disk behaves as analytically expected
    idf= dehnendf(beta=0.,profileParams=(1./3.,1.,0.0125))
    cp= 0.05
    pot= [LogarithmicHaloPotential(normalize=1.),
          EllipticalDiskPotential(cp=cp,sp=0.,p=0.,tform=-150.,tsteady=125.)]
    edf= evolveddiskdf(idf,pot=pot,to=-150.)
    #Should be 1.
    mvt, grid= edf.meanvT(0.9,phi=-numpy.pi/4.,
                          integrate_method='rk6_c',grid=True,
                          returnGrid=True,gridpoints=_GRIDPOINTS)
    assert numpy.fabs(mvt-1.) < 10.**-3., 'Cold elliptical disk does not agree with analytical calculation for vt'
    #Should be 1.-cp
    mvt, grid= edf.meanvT(0.9,phi=0.,
                          integrate_method='rk6_c',grid=True,nsigma=7.,
                          returnGrid=True,gridpoints=_GRIDPOINTS)
    assert numpy.fabs(mvt-1.+cp) < 10.**-3., 'Cold elliptical disk does not agree with analytical calculation for vt'
    return None
Exemplo n.º 40
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def test_mildnonaxi_sigmat2_grid():
    # Test that for a close to axisymmetric potential, the sigmaR2 is close to the value of the initial DF
    idf= dehnendf(beta=0.)
    pot= [LogarithmicHaloPotential(normalize=1.),
          SteadyLogSpiralPotential(A=-0.005,omegas=0.2)] #very mild non-axi
    edf= evolveddiskdf(idf,pot=pot,to=-10.)
    st2, grid= edf.sigmaT2(0.9,phi=0.2,integrate_method='rk6_c',grid=True,
                           returnGrid=True,gridpoints=_GRIDPOINTS)
    ist2= idf.sigmaT2(0.9)
    assert numpy.fabs(numpy.log(st2)-numpy.log(ist2)) < 0.025, 'sigmat2 of evolveddiskdf for axisymmetric potential is not equal to that of initial DF'
    st2= edf.sigmaT2(0.9,phi=0.2,integrate_method='rk6_c',grid=grid,
                     gridpoints=_GRIDPOINTS)
    assert numpy.fabs(numpy.log(st2)-numpy.log(ist2)) < 0.025, 'sigmat2 of evolveddiskdf for axisymmetric potential is not equal to that of initial DF when calculated with pre-computed grid'
    st2= edf.sigmaT2(0.9,phi=0.2,integrate_method='rk6_c',grid=grid,
                     meanvT=_maxi_meanvt,surfacemass=_maxi_surfacemass,
                     gridpoints=_GRIDPOINTS)
    assert numpy.fabs(numpy.log(st2)-numpy.log(ist2)) < 0.025, 'sigmat2 of evolveddiskdf for axisymmetric potential is not equal to that of initial DF when calculated with pre-computed grid and meanvR,surfacemass'
    global _maxi_sigmat2
    _maxi_sigmat2= st2
    return None
Exemplo n.º 41
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def test_mildnonaxi_sigmat2_grid():
    # Test that for a close to axisymmetric potential, the sigmaR2 is close to the value of the initial DF
    idf= dehnendf(beta=0.)
    pot= [LogarithmicHaloPotential(normalize=1.),
          SteadyLogSpiralPotential(A=-0.005,omegas=0.2)] #very mild non-axi
    edf= evolveddiskdf(idf,pot=pot,to=-10.)
    st2, grid= edf.sigmaT2(0.9,phi=0.2,integrate_method='rk6_c',grid=True,
                           returnGrid=True,gridpoints=_GRIDPOINTS)
    ist2= idf.sigmaT2(0.9)
    assert numpy.fabs(numpy.log(st2)-numpy.log(ist2)) < 0.025, 'sigmat2 of evolveddiskdf for axisymmetric potential is not equal to that of initial DF'
    st2= edf.sigmaT2(0.9,phi=0.2,integrate_method='rk6_c',grid=grid,
                     gridpoints=_GRIDPOINTS)
    assert numpy.fabs(numpy.log(st2)-numpy.log(ist2)) < 0.025, 'sigmat2 of evolveddiskdf for axisymmetric potential is not equal to that of initial DF when calculated with pre-computed grid'
    st2= edf.sigmaT2(0.9,phi=0.2,integrate_method='rk6_c',grid=grid,
                     meanvT=_maxi_meanvt,surfacemass=_maxi_surfacemass,
                     gridpoints=_GRIDPOINTS)
    assert numpy.fabs(numpy.log(st2)-numpy.log(ist2)) < 0.025, 'sigmat2 of evolveddiskdf for axisymmetric potential is not equal to that of initial DF when calculated with pre-computed grid and meanvR,surfacemass'
    global _maxi_sigmat2
    _maxi_sigmat2= st2
    return None
Exemplo n.º 42
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def test_mildnonaxi_sigmart_grid():
    # Test that for a close to axisymmetric potential, the sigmaR2 is close to zero
    idf= dehnendf(beta=0.)
    pot= [LogarithmicHaloPotential(normalize=1.),
          SteadyLogSpiralPotential(A=-0.005,omegas=0.2)] #very mild non-axi
    edf= evolveddiskdf(idf,pot=pot,to=-10.)
    srt, grid= edf.sigmaRT(0.9,phi=0.2,integrate_method='rk6_c',grid=True,
                           returnGrid=True,gridpoints=_GRIDPOINTS)
    assert numpy.fabs(srt) < 0.01, 'sigmart of evolveddiskdf for axisymmetric potential is not equal to zero'
    srt= edf.sigmaRT(0.9,phi=0.2,integrate_method='rk6_c',grid=grid,
                     gridpoints=_GRIDPOINTS)
    assert numpy.fabs(srt) < 0.01, 'sigmart of evolveddiskdf for axisymmetric potential is not equal zero when calculated with pre-computed grid'
    srt= edf.sigmaRT(0.9,phi=0.2,integrate_method='rk6_c',grid=grid,
                     meanvR=_maxi_meanvr,
                     meanvT=_maxi_meanvt,surfacemass=_maxi_surfacemass,
                     gridpoints=_GRIDPOINTS)
    assert numpy.fabs(srt) < 0.01, 'sigmart of evolveddiskdf for axisymmetric potential is not equal to zero when calculated with pre-computed grid and meanvR,surfacemass'
    global _maxi_sigmart
    _maxi_sigmart= srt
    return None
Exemplo n.º 43
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def test_mildnonaxi_oortA_grid_tlist():
    # Test that for a close to axisymmetric potential, the oortA is close to the value of the initial DF
    idf= dehnendf(beta=0.)
    pot= [LogarithmicHaloPotential(normalize=1.),
          EllipticalDiskPotential(twophio=0.001)] #very mild non-axi
    edf= evolveddiskdf(idf,pot=pot,to=-10.)
    oa, grid, dgridR, dgridphi=\
        edf.oortA(0.9,t=[0.,-2.5,-5.,-7.5,-10.],
                  phi=0.2,integrate_method='rk6_c',
                  grid=True,derivRGrid=True,derivphiGrid=True,
                  returnGrids=True,
                  gridpoints=_GRIDPOINTS,derivGridpoints=_GRIDPOINTS)
    ioa= idf.oortA(0.9)
    assert numpy.all(numpy.fabs(oa-ioa) < 0.005), 'oortA of evolveddiskdf for axisymmetric potential is not equal to that of initial DF'
    oa= edf.oortA(0.9,t=[0.,-2.5,-5.,-7.5,-10.],
                  phi=0.2,integrate_method='rk6_c',grid=grid,
                  derivRGrid=dgridR,derivphiGrid=dgridphi,
                  gridpoints=_GRIDPOINTS,derivGridpoints=_GRIDPOINTS)
    assert numpy.all(numpy.fabs(oa-ioa) < 0.005), 'oortA of evolveddiskdf for axisymmetric potential is not equal to that of initial DF when calculated with pre-computed grid'
    return None
Exemplo n.º 44
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def test_mildnonaxi_sigmart_grid():
    # Test that for a close to axisymmetric potential, the sigmaR2 is close to zero
    idf= dehnendf(beta=0.)
    pot= [LogarithmicHaloPotential(normalize=1.),
          SteadyLogSpiralPotential(A=-0.005,omegas=0.2)] #very mild non-axi
    edf= evolveddiskdf(idf,pot=pot,to=-10.)
    srt, grid= edf.sigmaRT(0.9,phi=0.2,integrate_method='rk6_c',grid=True,
                           returnGrid=True,gridpoints=_GRIDPOINTS)
    assert numpy.fabs(srt) < 0.01, 'sigmart of evolveddiskdf for axisymmetric potential is not equal to zero'
    srt= edf.sigmaRT(0.9,phi=0.2,integrate_method='rk6_c',grid=grid,
                     gridpoints=_GRIDPOINTS)
    assert numpy.fabs(srt) < 0.01, 'sigmart of evolveddiskdf for axisymmetric potential is not equal zero when calculated with pre-computed grid'
    srt= edf.sigmaRT(0.9,phi=0.2,integrate_method='rk6_c',grid=grid,
                     meanvR=_maxi_meanvr,
                     meanvT=_maxi_meanvt,surfacemass=_maxi_surfacemass,
                     gridpoints=_GRIDPOINTS)
    assert numpy.fabs(srt) < 0.01, 'sigmart of evolveddiskdf for axisymmetric potential is not equal to zero when calculated with pre-computed grid and meanvR,surfacemass'
    global _maxi_sigmart
    _maxi_sigmart= srt
    return None
Exemplo n.º 45
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oortB_array = np.empty(len(omegas))
oortC_array = np.empty(len(omegas))
oortK_array = np.empty(len(omegas))

for k in range(len(omegas)):
    sp = potential.SpiralArmsPotential(amp=amp30,
                                       N=N,
                                       alpha=alpha,
                                       H=H,
                                       Rs=Rs,
                                       omega=omegas[k],
                                       phi_ref=phi_ref)
    sp = potential.DehnenSmoothWrapperPotential(pot=sp,
                                                tform=tform,
                                                tsteady=tsteady)
    edf = evolveddiskdf(dfc, [lp, sp], to=sp._tform)

    oortA_array[k], grid, derivRGrid, derivphiGrid = edf.oortA(
        R=1,
        phi=0,
        gridpoints=n,
        derivGridpoints=n,
        grid=True,
        derivphiGrid=True,
        derivRGrid=True,
        nsigma=6.,
        returnGrids=True)
    oortB_array[k] = edf.oortB(R=1,
                               phi=0,
                               grid=grid,
                               derivphiGrid=derivphiGrid,
Exemplo n.º 46
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def pickle_data(filename, data):
    output = open(filename, 'wb')
    pickle.dump(data, output)
    output.close()


tform = -10 * u.Gyr
vel_disp = 40 / 220

lp = potential.LogarithmicHaloPotential(amp=1, normalize=1)
dfc = dehnendf(beta=0,
               profileParams=(1 / 3, 1, vel_disp),
               correct=True,
               niter=20)
edf = evolveddiskdf(dfc, lp, to=tform)

oortA, grid, derivRGrid, derivphiGrid = edf.oortA(R=1,
                                                  phi=0,
                                                  gridpoints=101,
                                                  derivGridpoints=101,
                                                  grid=True,
                                                  derivphiGrid=True,
                                                  derivRGrid=True,
                                                  nsigma=6.,
                                                  returnGrids=True)
oortB = edf.oortB(R=1,
                  phi=0,
                  grid=grid,
                  derivphiGrid=derivphiGrid,
                  derivRGrid=derivRGrid,