def test_hopping_generator():
"""Generated next-nearest hoppings should produce the same result as the builtin lattice"""
from scipy.spatial import cKDTree
@pb.hopping_generator("tnn_test", energy=graphene.t_nn)
def next_nearest(x, y, z):
pos = np.stack([x, y, z], axis=1)
dmin = graphene.a * 0.95
dmax = graphene.a * 1.05
kdtree = cKDTree(pos)
coo = kdtree.sparse_distance_matrix(kdtree, dmax).tocoo()
idx = coo.data > dmin
return coo.row[idx], coo.col[idx]
@pb.onsite_energy_modifier
def onsite_offset(energy):
return energy + 3 * graphene.t_nn
model = pb.Model(graphene.monolayer(), next_nearest, onsite_offset,
graphene.hexagon_ac(1))
expected = pb.Model(graphene.monolayer(2), graphene.hexagon_ac(1))
assert pytest.fuzzy_equal(model.hamiltonian, expected.hamiltonian)
@pb.hopping_generator("t_new", energy=1.0)
def bad_generator():
"""Different array lengths"""
return [0, 1, 2], [0, 1]
model = pb.Model(graphene.monolayer(), pb.primitive(3, 3), bad_generator)
with pytest.raises(RuntimeError) as excinfo:
model.eval()
assert "the number of `from` and `to` indices must be equal" in str(
excinfo.value)
def factory(v1, v2, energy=np.linspace(0, 0.1, 10)):
model = pb.Model(graphene.monolayer(),
graphene.hexagon_ac(side_width=15),
pb.constant_potential(v1), pb.constant_potential(v2))
kpm = pb.greens.kpm(model)
return kpm.deferred_ldos(energy, broadening=0.15, position=[0, 0])
def test_ldos_sublattice():
"""LDOS for A and B sublattices should be antisymmetric for graphene with a mass term"""
model = pb.Model(graphene.monolayer(), graphene.hexagon_ac(10), graphene.mass_term(1))
kpm = pb.greens.kpm(model)
a, b = (kpm.calc_ldos(np.linspace(-5, 5, 50), 0.1, [0, 0], sub) for sub in ('A', 'B'))
assert pytest.fuzzy_equal(a.ldos, b.ldos[::-1], rtol=1e-3, atol=1e-6)
def test_ldos_sublattice():
"""LDOS for A and B sublattices should be antisymmetric for graphene with a mass term"""
model = pb.Model(graphene.monolayer(), graphene.hexagon_ac(10), graphene.mass_term(1))
kpm = pb.chebyshev.kpm(model)
a, b = (kpm.calc_ldos(np.linspace(-5, 5, 50), 0.1, [0, 0], sub) for sub in ('A', 'B'))
assert pytest.fuzzy_equal(a.ldos, b.ldos[::-1], rtol=1e-3, atol=1e-6)
def factory(v, energy=np.linspace(0, 0.1, 10)):
model = pb.Model(
graphene.monolayer(),
graphene.hexagon_ac(side_width=15),
pb.constant_potential(v)
)
kpm = pb.greens.kpm(model)
return kpm.deferred_ldos(energy, broadening=0.15, position=[0, 0])
def factory(v, energy=np.linspace(0, 0.1, 10)):
model = pb.Model(graphene.monolayer(),
graphene.hexagon_ac(side_width=15),
pb.constant_potential(v))
kpm = pb.kpm(model, kernel=pb.lorentz_kernel())
return kpm.deferred_ldos(energy,
broadening=0.15,
position=[0, 0],
sublattice="B")
def test_optimized_hamiltonian():
"""Currently available only in internal interface"""
from pybinding import _cpp
model = pb.Model(graphene.monolayer(), graphene.hexagon_ac(10))
h = model.hamiltonian
oh = _cpp.OptimizedHamiltonian(model.raw_hamiltonian, 0)
assert oh.matrix.shape == h.shape
assert oh.sizes[-1] == h.shape[0]
assert len(oh.indices) == h.shape[0]
def test_optimized_hamiltonian():
"""Currently available only in internal interface"""
from pybinding import _cpp
model = pb.Model(graphene.monolayer(), graphene.hexagon_ac(10))
h = model.hamiltonian
oh = _cpp.OptimizedHamiltonian(model.raw_hamiltonian, 0)
assert oh.matrix.shape == h.shape
assert oh.sizes[-1] == h.shape[0]
assert len(oh.indices) == h.shape[0]
def test_kpm_reuse():
"""KPM should return the same result when a single object is used for multiple calculations"""
model = pb.Model(graphene.monolayer(), graphene.hexagon_ac(10))
kpm = pb.greens.kpm(model)
energy = np.linspace(-5, 5, 50)
broadening = 0.1
for position in [0, 0], [6, 0]:
actual = kpm.calc_ldos(energy, broadening, position)
expected = pb.greens.kpm(model).calc_ldos(energy, broadening, position)
assert pytest.fuzzy_equal(actual, expected, rtol=1e-3, atol=1e-6)
def test_kpm_reuse():
"""KPM should return the same result when a single object is used for multiple calculations"""
model = pb.Model(graphene.monolayer(), graphene.hexagon_ac(10))
kpm = pb.chebyshev.kpm(model)
energy = np.linspace(-5, 5, 50)
broadening = 0.1
for position in [0, 0], [6, 0]:
actual = kpm.calc_ldos(energy, broadening, position)
expected = pb.chebyshev.kpm(model).calc_ldos(energy, broadening, position)
assert pytest.fuzzy_equal(actual, expected, rtol=1e-3, atol=1e-6)
def test_hopping_generator():
"""Generated next-nearest hoppings should produce the same result as the builtin lattice"""
from scipy.spatial import cKDTree
@pb.hopping_generator("tnn_test", energy=graphene.t_nn)
def next_nearest(x, y, z):
pos = np.stack([x, y, z], axis=1)
dmin = graphene.a * 0.95
dmax = graphene.a * 1.05
kdtree = cKDTree(pos)
coo = kdtree.sparse_distance_matrix(kdtree, dmax).tocoo()
idx = coo.data > dmin
return coo.row[idx], coo.col[idx]
@pb.onsite_energy_modifier
def onsite_offset(energy):
return energy + 3 * graphene.t_nn
model = pb.Model(graphene.monolayer(), next_nearest, onsite_offset, graphene.hexagon_ac(1))
expected = pb.Model(graphene.monolayer(2), graphene.hexagon_ac(1))
assert pytest.fuzzy_equal(model.hamiltonian, expected.hamiltonian)
def test_site_generator():
"""Generated some disordered sites"""
@pb.site_generator("New", energy=0.4)
def site_gen():
x = [10, 20, 30]
y = [1, 2, 3]
z = [0, -1, -2]
return x, y, z
model = pb.Model(graphene.monolayer(), graphene.hexagon_ac(1), site_gen)
s = model.system[model.system.x >= 10]
assert s.num_sites == 3
assert pytest.fuzzy_equal(s.x, [10, 20, 30])
assert pytest.fuzzy_equal(s.y, [1, 2, 3])
assert pytest.fuzzy_equal(s.z, [0, -1, -2])
import pybinding as pb
from pybinding.repository import graphene
import matplotlib.pyplot as plt
from math import pi
pb.pltutils.use_style()
model = pb.Model(
graphene.monolayer(),
pb.rectangle(x=2, y=1.2)
)
model.plot()
plt.show()
model = pb.Model(
graphene.monolayer(),
pb.regular_polygon(num_sides=6, radius=1.4, angle=pi/6)
)
model.plot()
plt.show()
# A graphene-specific shape which guaranties armchair edges on all sides
model = pb.Model(
graphene.bilayer(),
graphene.hexagon_ac(side_width=1)
)
model.plot()
plt.show()
import pytest
import pybinding as pb
from pybinding.repository import graphene
models = {
'graphene-monolayer': [graphene.monolayer(), graphene.hexagon_ac(1)],
'graphene-monolayer-alt': [graphene.monolayer_alt(), pb.rectangle(1.6, 1.4)],
'graphene-monolayer-4atom': [graphene.monolayer_4atom()],
'graphene-monolayer-nn': [graphene.monolayer(2), pb.regular_polygon(6, 0.9)],
'graphene-monolayer-periodic-1d': [graphene.monolayer(), pb.primitive(5, 5),
pb.translational_symmetry(a1=True, a2=False)],
'graphene-monolayer-periodic-1d-alt': [graphene.monolayer_4atom(), pb.rectangle(1),
pb.translational_symmetry(a1=False, a2=0.6)],
'graphene-monolayer-periodic-2d': [graphene.monolayer(), pb.primitive(a1=5, a2=5),
pb.translational_symmetry(a1=1, a2=1)],
'graphene-monolayer-4atom-periodic-2d': [graphene.monolayer_4atom(), pb.rectangle(1),
pb.translational_symmetry(a1=0.6, a2=0.6)],
'graphene-bilayer': [graphene.bilayer(), graphene.hexagon_ac(0.6)],
}
@pytest.fixture(scope='module', ids=list(models.keys()), params=models.values())
def model(request):
return pb.Model(*request.param)
def test_api():
model = pb.Model(graphene.monolayer(), pb.primitive(2, 2))
system = model.system
def test_system_plot(compare_figure):
model = pb.Model(graphene.bilayer(), graphene.hexagon_ac(0.1))
with compare_figure() as chk:
model.system.plot()
assert chk.passed
import pytest
import numpy as np
import pybinding as pb
from pybinding.repository import graphene, group6_tmd
models = {
'graphene-monolayer': [graphene.monolayer(), graphene.hexagon_ac(1)],
'graphene-monolayer-alt': [graphene.monolayer_alt(), pb.rectangle(1.6, 1.4)],
'graphene-monolayer-4atom': [graphene.monolayer_4atom()],
'graphene-monolayer-nn': [graphene.monolayer(2), pb.regular_polygon(6, 0.9)],
'graphene-monolayer-periodic-1d': [graphene.monolayer(), pb.primitive(5, 5),
pb.translational_symmetry(a1=True, a2=False)],
'graphene-monolayer-periodic-1d-alt': [graphene.monolayer_4atom(), pb.rectangle(1),
pb.translational_symmetry(a1=False, a2=0.6)],
'graphene-monolayer-periodic-2d': [graphene.monolayer(), pb.primitive(a1=5, a2=5),
pb.translational_symmetry(a1=1, a2=1)],
'graphene-monolayer-4atom-periodic-2d': [graphene.monolayer_4atom(), pb.rectangle(1),
pb.translational_symmetry(a1=0.6, a2=0.6)],
'graphene-bilayer': [graphene.bilayer(), graphene.hexagon_ac(0.6)],
}
@pytest.fixture(scope='module', ids=list(models.keys()), params=models.values())
def model(request):
return pb.Model(*request.param)
def test_pickle_round_trip(model):
import pickle
unpickled = pickle.loads(pickle.dumps(model.system))
import pytest
import numpy as np
import pybinding as pb
from pybinding.repository import graphene, group6_tmd
models = {
'graphene-monolayer': [graphene.monolayer(),
graphene.hexagon_ac(1)],
'graphene-monolayer-alt':
[graphene.monolayer_alt(),
pb.rectangle(1.6, 1.4)],
'graphene-monolayer-4atom': [graphene.monolayer_4atom()],
'graphene-monolayer-nn':
[graphene.monolayer(2), pb.regular_polygon(6, 0.9)],
'graphene-monolayer-periodic-1d': [
graphene.monolayer(),
pb.primitive(5, 5),
pb.translational_symmetry(a1=True, a2=False)
],
'graphene-monolayer-periodic-1d-alt': [
graphene.monolayer_4atom(),
pb.rectangle(1),
pb.translational_symmetry(a1=False, a2=0.6)
],
'graphene-monolayer-periodic-2d': [
graphene.monolayer(),
pb.primitive(a1=5, a2=5),
pb.translational_symmetry(a1=1, a2=1)
],
'graphene-monolayer-4atom-periodic-2d': [
def test_system_plot(compare_figure):
model = pb.Model(graphene.bilayer(), graphene.hexagon_ac(0.1))
with compare_figure() as chk:
model.system.plot()
assert chk.passed
#!/usr/bin python # -*- encoding: utf-8 -*- ''' @Author : Celeste Young @File : 石墨烯单层.py @Time : 2021/9/18 16:25 @E-mail : [email protected] @Tips : ''' """Several finite-sized systems created using builtin lattices and shapes""" import pybinding as pb from pybinding.repository import graphene import matplotlib.pyplot as plt from math import pi pb.pltutils.use_style() model_1 = pb.Model( graphene.monolayer(), # pb.rectangle(x=2, y=1.2) pb.regular_polygon(num_sides=6, radius=1.4)) model_2 = pb.Model(graphene.bilayer(), graphene.hexagon_ac(side_width=1)) model_3 = pb.Model( graphene.monolayer(), # pb.rectangle(x=2, y=1.2) pb.regular_polygon(num_sides=6, radius=1.4, angle=0)) model_2.plot() # model_2.plot() plt.show()
