本文整理汇总了Python中fidimag.atomistic.Sim.skyrmion_number方法的典型用法代码示例。如果您正苦于以下问题：Python Sim.skyrmion_number方法的具体用法？Python Sim.skyrmion_number怎么用？Python Sim.skyrmion_number使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类fidimag.atomistic.Sim的用法示例。
在下文中一共展示了Sim.skyrmion_number方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_skx_num
# 需要导入模块: from fidimag.atomistic import Sim [as 别名]
# 或者: from fidimag.atomistic.Sim import skyrmion_number [as 别名]
def test_skx_num():
mesh = CuboidMesh(nx=120, ny=120, nz=1, periodicity=(True, True, False))
sim = Sim(mesh, name='skx_num')
sim.set_tols(rtol=1e-6, atol=1e-6)
sim.alpha = 1.0
sim.gamma = 1.0
sim.mu_s = 1.0
sim.set_m(init_m)
sim.do_procession = False
J = 1.0
exch = UniformExchange(J)
sim.add(exch)
D = 0.09
dmi = DMI(D)
sim.add(dmi)
zeeman = Zeeman([0, 0, 5e-3])
sim.add(zeeman)
sim.relax(dt=2.0, stopping_dmdt=1e-2, max_steps=1000,
save_m_steps=None, save_vtk_steps=None)
skn = sim.skyrmion_number()
print 'skx_number', skn
assert skn > -1 and skn < -0.99示例2: test_skx_num_atomistic
# 需要导入模块: from fidimag.atomistic import Sim [as 别名]
# 或者: from fidimag.atomistic.Sim import skyrmion_number [as 别名]
def test_skx_num_atomistic():
"""
Test the *finite spin chirality* or skyrmion number for
a discrete spins simulation in a two dimensional lattice
The expression is (PRL 108, 017601 (2012)) :
Q = S_i \dot ( S_{i+1} X S_{j+1} )
+ S_i \dot ( S_{i-1} X S_{j-1} )
which measures the chirality taking two triangles of spins
per lattice site i:
S_{i} , S_{i + x} , S_{i + y} and
S_{i} , S_{i - x} , S_{i - y}
The area of the two triangles cover a unit cell, thus the sum
cover the whole area of the atomic lattice
This test generate a skyrmion pointing down with unrealistic
paremeters.
"""
mesh = CuboidMesh(nx=120, ny=120, nz=1,
periodicity=(True, True, False))
sim = Sim(mesh, name='skx_num')
sim.set_tols(rtol=1e-6, atol=1e-6)
sim.alpha = 1.0
sim.gamma = 1.0
sim.mu_s = 1.0
sim.set_m(lambda pos: init_m(pos, 60, 60, 20))
sim.do_precession = False
J = 1.0
exch = UniformExchange(J)
sim.add(exch)
D = 0.09
dmi = DMI(D)
sim.add(dmi)
zeeman = Zeeman([0, 0, 5e-3])
sim.add(zeeman)
sim.relax(dt=2.0, stopping_dmdt=1e-2, max_steps=1000,
save_m_steps=None, save_vtk_steps=None)
skn = sim.skyrmion_number()
print('skx_number', skn)
assert skn > -1 and skn < -0.99示例3: test_skx_num_atomistic
# 需要导入模块: from fidimag.atomistic import Sim [as 别名]
# 或者: from fidimag.atomistic.Sim import skyrmion_number [as 别名]
def test_skx_num_atomistic():
"""
Test the *finite spin chirality* or skyrmion number for
a discrete spins simulation in a two dimensional lattice
The expression is (PRL 108, 017601 (2012)) :
Q = S_i \dot ( S_{i+1} X S_{j+1} )
+ S_i \dot ( S_{i-1} X S_{j-1} )
which measures the chirality taking two triangles of spins
per lattice site i:
S_{i} , S_{i + x} , S_{i + y} and
S_{i} , S_{i - x} , S_{i - y}
The area of the two triangles cover a unit cell, thus the sum
cover the whole area of the atomic lattice
We also test the Berg and Luscher definition for a topological
charge (see the hexagonal mesh test for details) in a
square lattice.
This test generate a skyrmion pointing down with unrealistic
paremeters.
"""
mesh = CuboidMesh(nx=120, ny=120, nz=1,
periodicity=(True, True, False))
sim = Sim(mesh, name='skx_num')
sim.driver.set_tols(rtol=1e-6, atol=1e-6)
sim.driver.alpha = 1.0
sim.driver.gamma = 1.0
sim.mu_s = 1.0
sim.set_m(lambda pos: init_m(pos, 60, 60, 20))
sim.do_precession = False
J = 1.0
exch = UniformExchange(J)
sim.add(exch)
D = 0.09
dmi = DMI(D)
sim.add(dmi)
zeeman = Zeeman([0, 0, 5e-3])
sim.add(zeeman)
sim.relax(dt=2.0, stopping_dmdt=1e-2, max_steps=1000,
save_m_steps=None, save_vtk_steps=None)
skn = sim.skyrmion_number()
print('skx_number', skn)
skn_BL = sim.skyrmion_number(method='BergLuscher')
print('skx_number_BergLuscher', skn_BL)
# Test the finite chirality method
assert skn > -1 and skn < -0.99
# Test the Berg-Luscher method
assert np.abs(skn_BL - (-1)) < 1e-4 and np.sign(skn_BL) < 0
# Test guiding center
Rx, Ry = compute_RxRy(mesh, sim.spin)
print('Rx=%g, Ry=%g'%(Rx, Ry))
assert Rx<60 and Rx>58
assert Ry<60 and Ry>58示例4: test_skx_num_atomistic_hexagonal
# 需要导入模块: from fidimag.atomistic import Sim [as 别名]
# 或者: from fidimag.atomistic.Sim import skyrmion_number [as 别名]
def test_skx_num_atomistic_hexagonal():
"""
Test the topological charge or skyrmion number for a discrete spins
simulation in a two dimensional hexagonal lattice, using Berg and Luscher
definition in [Nucl Phys B 190, 412 (1981)] and simplified in [PRB 93,
174403 (2016)], which maps a triangulated lattice (using triangles of
neighbouring spins) area into a unit sphere.
The areas of two triangles per lattice site cover a unit cell, thus the sum
cover the whole area of the atomic lattice
This test generates a skyrmion pointing down and two skyrmions pointing up
in a PdFe sample using magnetic parameters from: PRL 114, 177203 (2015)
"""
mesh = HexagonalMesh(0.2715, 41, 41, periodicity=(True, True))
sim = Sim(mesh, name='skx_number_hexagonal')
sim.driver.set_tols(rtol=1e-6, atol=1e-6)
sim.driver.alpha = 1.0
sim.driver.gamma = 1.0
sim.mu_s = 3 * const.mu_B
sim.set_m(lambda pos: init_m(pos, 16.1, 10, 2))
sim.driver.do_precession = False
J = 5.881 * const.meV
exch = UniformExchange(J)
sim.add(exch)
D = 1.557 * const.meV
dmi = DMI(D, dmi_type='interfacial')
sim.add(dmi)
sim.add(Anisotropy(0.406 * const.meV, axis=[0, 0, 1]))
zeeman = Zeeman([0, 0, 2.5])
sim.add(zeeman)
sim.relax(dt=1e-13, stopping_dmdt=1e-2, max_steps=2000,
save_m_steps=None, save_vtk_steps=100)
skn_single = sim.skyrmion_number(method='BergLuscher')
print('skx_number_hexagonal', skn_single)
# Now we generate two skyrmions pointing up
sim.driver.reset_integrator()
sim.set_m(lambda pos: init_m_multiple_sks(pos, 1,
sk_pos=[(9, 6), (18, 12)]
)
)
sim.get_interaction('Zeeman').update_field([0, 0, -2.5])
sim.relax(dt=1e-13, stopping_dmdt=1e-2, max_steps=2000,
save_m_steps=None, save_vtk_steps=None)
skn_two = sim.skyrmion_number(method='BergLuscher')
print('skx_number_hexagonal_two', skn_two)
# Check that we get a right sk number
assert np.abs(skn_single - (-1)) < 1e-4 and np.sign(skn_single) < 0
assert np.abs(skn_two - (2)) < 1e-4 and np.sign(skn_two) > 0