def test_madx_kicker(x, px, y, py, pt, beta, length, tilt, hkick, vkick):
k = Kinematics(beta)
mi = manzoni.Input(sequence=[
manzoni.Kicker(
'K1',
integrator=MadXIntegrator,
L=length * _.m,
TILT=tilt * _.radian,
HKICK=hkick * _.radian,
VKICK=vkick * _.radian,
),
]).freeze()
deltap = np.sqrt(pt**2 + 2 * pt / beta + 1) - 1
distribution = np.array([
[x, px, y, py, deltap, pt],
])
beam = manzoni.Beam(kinematics=k, distribution=distribution)
observer = manzoni.BeamObserver()
manzoni.track(beamline=mi, beam=beam, observer=observer)
r = observer.to_df().iloc[-1]['BEAM_OUT']
assert np.all(
np.isclose(
get_madx_tracking_data_kicker(x, px, y, py, pt, beta, length, tilt,
hkick, vkick), r[:, 0:4]))
def test_madx_dipedge(x, px, y, py, pt, beta, h, e1, hgap, fint):
k = Kinematics(beta)
mi = manzoni.Input(sequence=[
manzoni.Drift('D1', L=0.5 * _.m),
manzoni.DipEdge(
'DE1',
integrator=MadXIntegrator,
H=h * _.m**-1,
E1=e1 * _.radian,
HGAP=hgap * _.m,
FINT=fint,
),
manzoni.Drift('D2', L=0.5 * _.m),
]).freeze()
deltap = np.sqrt(pt**2 + 2 * pt / beta + 1) - 1
distribution = np.array([
[x, px, y, py, deltap, pt],
])
beam = manzoni.Beam(kinematics=k, distribution=distribution)
observer = manzoni.BeamObserver()
manzoni.track(beamline=mi, beam=beam, observer=observer)
r = observer.to_df().iloc[-1]['BEAM_OUT']
assert np.all(
np.isclose(
get_madx_tracking_data_dipedge(x, px, y, py, pt, beta, h, e1, hgap,
fint), r[:, 0:4]))
def test_madx_rbend_fringe(x, px, y, py, pt, beta, length, angle, k1, e1, e2,
hgap, fint, fintx):
k = Kinematics(beta)
mi_rbend = manzoni.Input(sequence=[
manzoni.RBend(
'B1',
integrator=MadXIntegrator,
L=length * _.m,
K1=k1 * _.m**-2,
ANGLE=angle * _.radian,
E1=e1 * _.radian,
E2=e2 * _.radian,
HGAP=hgap * _.m,
FINT=fint,
FINTX=fintx,
),
]).freeze()
deltap = np.sqrt(pt**2 + 2 * pt / beta + 1) - 1
distribution = np.array([
[x, px, y, py, deltap, pt],
])
beam = manzoni.Beam(kinematics=k, distribution=distribution)
observer = manzoni.BeamObserver()
manzoni.track(beamline=mi_rbend, beam=beam, observer=observer)
r_rbend = observer.to_df().iloc[-1]['BEAM_OUT']
assert np.all(
np.isclose(
get_madx_tracking_data_bend(x, px, y, py, pt, beta, length, angle,
k1, e1, e2, hgap, fint, fintx,
'RBEND'), r_rbend[:, 0:4]))
def test_madx_drift(x, px, y, py, pt, beta, length):
k = Kinematics(beta)
mi = manzoni.Input(sequence=[
manzoni.Drift('B1', integrator=MadXIntegrator, L=length * _.m),
]).freeze()
deltap = np.sqrt(pt**2 + 2 * pt / beta + 1) - 1
distribution = np.array([
[x, px, y, py, deltap, pt],
])
beam = manzoni.Beam(kinematics=k, distribution=distribution)
observer = manzoni.BeamObserver()
manzoni.track(beamline=mi, beam=beam, observer=observer)
r = observer.to_df().iloc[-1]['BEAM_OUT']
assert np.all(
np.isclose(
get_madx_tracking_data_drift(x, px, y, py, pt, beta, length),
r[:, 0:4]))
def test_madx_quadrupole(x, px, y, py, pt, beta, length, k1, k1s, tilt):
k = Kinematics(beta)
mi = manzoni.Input(sequence=[
manzoni.Quadrupole('B1',
integrator=MadXIntegrator,
L=length * _.m,
K1=k1 * _.m**-2,
K1S=k1s * _.m**-2,
TILT=tilt * _.radian),
]).freeze()
deltap = np.sqrt(pt**2 + 2 * pt / beta + 1) - 1
distribution = np.array([
[x, px, y, py, deltap, pt],
])
beam = manzoni.Beam(kinematics=k, distribution=distribution)
observer = manzoni.BeamObserver()
manzoni.track(beamline=mi, beam=beam, observer=observer)
r = observer.to_df().iloc[-1]['BEAM_OUT']
assert np.all(
np.isclose(
get_madx_tracking_data_quadrupole(x, px, y, py, pt, beta, length,
k1, k1s, tilt), r[:, 0:4]))