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import math

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guanjihuan 2022-02-26 23:12:18 +08:00
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PyPI/setup.cfg
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@ -1,7 +1,7 @@
[metadata] [metadata]
# replace with your username: # replace with your username:
name = guan name = guan
version = 0.0.74 version = 0.0.75
author = guanjihuan author = guanjihuan
author_email = guanjihuan@163.com author_email = guanjihuan@163.com
description = An open source python package description = An open source python package

80
PyPI/src/guan/__init__.py
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@ -19,7 +19,7 @@
# import packages # import packages
import numpy as np import numpy as np
from math import * import math
import cmath import cmath
import copy import copy
import guan import guan
@ -359,12 +359,12 @@ def hamiltonian_of_ssh_model(k, v=0.6, w=1):
hamiltonian[1,0] = v+w*cmath.exp(1j*k) hamiltonian[1,0] = v+w*cmath.exp(1j*k)
return hamiltonian return hamiltonian
def hamiltonian_of_graphene(k1, k2, M=0, t=1, a=1/sqrt(3)): def hamiltonian_of_graphene(k1, k2, M=0, t=1, a=1/math.sqrt(3)):
h0 = np.zeros((2, 2), dtype=complex) # mass term h0 = np.zeros((2, 2), dtype=complex) # mass term
h1 = np.zeros((2, 2), dtype=complex) # nearest hopping h1 = np.zeros((2, 2), dtype=complex) # nearest hopping
h0[0, 0] = M h0[0, 0] = M
h0[1, 1] = -M h0[1, 1] = -M
h1[1, 0] = t*(cmath.exp(1j*k2*a)+cmath.exp(1j*sqrt(3)/2*k1*a-1j/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j/2*k2*a)) h1[1, 0] = t*(cmath.exp(1j*k2*a)+cmath.exp(1j*math.sqrt(3)/2*k1*a-1j/2*k2*a)+cmath.exp(-1j*math.sqrt(3)/2*k1*a-1j/2*k2*a))
h1[0, 1] = h1[1, 0].conj() h1[0, 1] = h1[1, 0].conj()
hamiltonian = h0 + h1 hamiltonian = h0 + h1
return hamiltonian return hamiltonian
@ -392,20 +392,20 @@ def hamiltonian_of_graphene_with_zigzag_in_quasi_one_dimension(k, N=10, M=0, t=1
hamiltonian = guan.one_dimensional_fourier_transform(k, unit_cell=h00, hopping=h01) hamiltonian = guan.one_dimensional_fourier_transform(k, unit_cell=h00, hopping=h01)
return hamiltonian return hamiltonian
def hamiltonian_of_haldane_model(k1, k2, M=2/3, t1=1, t2=1/3, phi=pi/4, a=1/sqrt(3)): def hamiltonian_of_haldane_model(k1, k2, M=2/3, t1=1, t2=1/3, phi=math.pi/4, a=1/math.sqrt(3)):
h0 = np.zeros((2, 2), dtype=complex) # mass term h0 = np.zeros((2, 2), dtype=complex) # mass term
h1 = np.zeros((2, 2), dtype=complex) # nearest hopping h1 = np.zeros((2, 2), dtype=complex) # nearest hopping
h2 = np.zeros((2, 2), dtype=complex) # next nearest hopping h2 = np.zeros((2, 2), dtype=complex) # next nearest hopping
h0[0, 0] = M h0[0, 0] = M
h0[1, 1] = -M h0[1, 1] = -M
h1[1, 0] = t1*(cmath.exp(1j*k2*a)+cmath.exp(1j*sqrt(3)/2*k1*a-1j/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j/2*k2*a)) h1[1, 0] = t1*(cmath.exp(1j*k2*a)+cmath.exp(1j*math.sqrt(3)/2*k1*a-1j/2*k2*a)+cmath.exp(-1j*math.sqrt(3)/2*k1*a-1j/2*k2*a))
h1[0, 1] = h1[1, 0].conj() h1[0, 1] = h1[1, 0].conj()
h2[0, 0] = t2*cmath.exp(-1j*phi)*(cmath.exp(1j*sqrt(3)*k1*a)+cmath.exp(-1j*sqrt(3)/2*k1*a+1j*3/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a)) h2[0, 0] = t2*cmath.exp(-1j*phi)*(cmath.exp(1j*math.sqrt(3)*k1*a)+cmath.exp(-1j*math.sqrt(3)/2*k1*a+1j*3/2*k2*a)+cmath.exp(-1j*math.sqrt(3)/2*k1*a-1j*3/2*k2*a))
h2[1, 1] = t2*cmath.exp(1j*phi)*(cmath.exp(1j*sqrt(3)*k1*a)+cmath.exp(-1j*sqrt(3)/2*k1*a+1j*3/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a)) h2[1, 1] = t2*cmath.exp(1j*phi)*(cmath.exp(1j*math.sqrt(3)*k1*a)+cmath.exp(-1j*math.sqrt(3)/2*k1*a+1j*3/2*k2*a)+cmath.exp(-1j*math.sqrt(3)/2*k1*a-1j*3/2*k2*a))
hamiltonian = h0 + h1 + h2 + h2.transpose().conj() hamiltonian = h0 + h1 + h2 + h2.transpose().conj()
return hamiltonian return hamiltonian
def hamiltonian_of_haldane_model_in_quasi_one_dimension(k, N=10, M=2/3, t1=1, t2=1/3, phi=pi/4): def hamiltonian_of_haldane_model_in_quasi_one_dimension(k, N=10, M=2/3, t1=1, t2=1/3, phi=math.pi/4):
h00 = np.zeros((4*N, 4*N), dtype=complex) # hopping in a unit cell h00 = np.zeros((4*N, 4*N), dtype=complex) # hopping in a unit cell
h01 = np.zeros((4*N, 4*N), dtype=complex) # hopping between unit cells h01 = np.zeros((4*N, 4*N), dtype=complex) # hopping between unit cells
for i in range(N): for i in range(N):
@ -448,10 +448,10 @@ def hamiltonian_of_haldane_model_in_quasi_one_dimension(k, N=10, M=2/3, t1=1, t2
def hamiltonian_of_one_QAH_model(k1, k2, t1=1, t2=1, t3=0.5, m=-1): def hamiltonian_of_one_QAH_model(k1, k2, t1=1, t2=1, t3=0.5, m=-1):
hamiltonian = np.zeros((2, 2), dtype=complex) hamiltonian = np.zeros((2, 2), dtype=complex)
hamiltonian[0, 1] = 2*t1*cos(k1)-1j*2*t1*cos(k2) hamiltonian[0, 1] = 2*t1*math.cos(k1)-1j*2*t1*math.cos(k2)
hamiltonian[1, 0] = 2*t1*cos(k1)+1j*2*t1*cos(k2) hamiltonian[1, 0] = 2*t1*math.cos(k1)+1j*2*t1*math.cos(k2)
hamiltonian[0, 0] = m+2*t3*sin(k1)+2*t3*sin(k2)+2*t2*cos(k1+k2) hamiltonian[0, 0] = m+2*t3*math.sin(k1)+2*t3*math.sin(k2)+2*t2*math.cos(k1+k2)
hamiltonian[1, 1] = -(m+2*t3*sin(k1)+2*t3*sin(k2)+2*t2*cos(k1+k2)) hamiltonian[1, 1] = -(m+2*t3*math.sin(k1)+2*t3*math.sin(k2)+2*t2*math.cos(k1+k2))
return hamiltonian return hamiltonian
def hamiltonian_of_BBH_model(kx, ky, gamma_x=0.5, gamma_y=0.5, lambda_x=1, lambda_y=1): def hamiltonian_of_BBH_model(kx, ky, gamma_x=0.5, gamma_y=0.5, lambda_x=1, lambda_y=1):
@ -614,13 +614,13 @@ def rotation_of_degenerate_vectors(vector1, vector2, index1, index2, precision=0
vector1 = np.array(vector1) vector1 = np.array(vector1)
vector2 = np.array(vector2) vector2 = np.array(vector2)
if np.abs(vector1[index2])>criterion or np.abs(vector2[index1])>criterion: if np.abs(vector1[index2])>criterion or np.abs(vector2[index1])>criterion:
for theta in np.arange(0, 2*pi, precision): for theta in np.arange(0, 2*math.pi, precision):
if show_theta==1: if show_theta==1:
print(theta) print(theta)
for phi1 in np.arange(0, 2*pi, precision): for phi1 in np.arange(0, 2*math.pi, precision):
for phi2 in np.arange(0, 2*pi, precision): for phi2 in np.arange(0, 2*math.pi, precision):
vector1_test = cmath.exp(1j*phi1)*vector1*cos(theta)+cmath.exp(1j*phi2)*vector2*sin(theta) vector1_test = cmath.exp(1j*phi1)*vector1*math.cos(theta)+cmath.exp(1j*phi2)*vector2*math.sin(theta)
vector2_test = -cmath.exp(-1j*phi2)*vector1*sin(theta)+cmath.exp(-1j*phi1)*vector2*cos(theta) vector2_test = -cmath.exp(-1j*phi2)*vector1*math.sin(theta)+cmath.exp(-1j*phi1)*vector2*math.cos(theta)
if np.abs(vector1_test[index2])<criterion and np.abs(vector2_test[index1])<criterion: if np.abs(vector1_test[index2])<criterion and np.abs(vector2_test[index1])<criterion:
vector1 = vector1_test vector1 = vector1_test
vector2 = vector2_test vector2 = vector2_test
@ -770,7 +770,7 @@ def electron_correlation_function_green_n_for_local_current(fermi_energy, h00, h
def total_density_of_states(fermi_energy, hamiltonian, broadening=0.01): def total_density_of_states(fermi_energy, hamiltonian, broadening=0.01):
green = guan.green_function(fermi_energy, hamiltonian, broadening) green = guan.green_function(fermi_energy, hamiltonian, broadening)
total_dos = -np.trace(np.imag(green))/pi total_dos = -np.trace(np.imag(green))/math.pi
return total_dos return total_dos
def total_density_of_states_with_fermi_energy_array(fermi_energy_array, hamiltonian, broadening=0.01, print_show=0): def total_density_of_states_with_fermi_energy_array(fermi_energy_array, hamiltonian, broadening=0.01, print_show=0):
@ -791,7 +791,7 @@ def local_density_of_states_for_square_lattice(fermi_energy, hamiltonian, N1, N2
for i1 in range(N1): for i1 in range(N1):
for i2 in range(N2): for i2 in range(N2):
for i in range(internal_degree): for i in range(internal_degree):
local_dos[i2, i1] = local_dos[i2, i1]-np.imag(green[i1*N2*internal_degree+i2*internal_degree+i, i1*N2*internal_degree+i2*internal_degree+i])/pi local_dos[i2, i1] = local_dos[i2, i1]-np.imag(green[i1*N2*internal_degree+i2*internal_degree+i, i1*N2*internal_degree+i2*internal_degree+i])/math.pi
return local_dos return local_dos
def local_density_of_states_for_cubic_lattice(fermi_energy, hamiltonian, N1, N2, N3, internal_degree=1, broadening=0.01): def local_density_of_states_for_cubic_lattice(fermi_energy, hamiltonian, N1, N2, N3, internal_degree=1, broadening=0.01):
@ -802,7 +802,7 @@ def local_density_of_states_for_cubic_lattice(fermi_energy, hamiltonian, N1, N2,
for i2 in range(N2): for i2 in range(N2):
for i3 in range(N3): for i3 in range(N3):
for i in range(internal_degree): for i in range(internal_degree):
local_dos[i3, i2, i1] = local_dos[i3, i2, i1]-np.imag(green[i1*N2*N3*internal_degree+i2*N3*internal_degree+i3*internal_degree+i, i1*N2*N3*internal_degree+i2*N3*internal_degree+i3*internal_degree+i])/pi local_dos[i3, i2, i1] = local_dos[i3, i2, i1]-np.imag(green[i1*N2*N3*internal_degree+i2*N3*internal_degree+i3*internal_degree+i, i1*N2*N3*internal_degree+i2*N3*internal_degree+i3*internal_degree+i])/math.pi
return local_dos return local_dos
def local_density_of_states_for_square_lattice_using_dyson_equation(fermi_energy, h00, h01, N2, N1, internal_degree=1, broadening=0.01): def local_density_of_states_for_square_lattice_using_dyson_equation(fermi_energy, h00, h01, N2, N1, internal_degree=1, broadening=0.01):
@ -832,7 +832,7 @@ def local_density_of_states_for_square_lattice_using_dyson_equation(fermi_energy
green_ni_n_minus = green_ni_n green_ni_n_minus = green_ni_n
for i2 in range(N2): for i2 in range(N2):
for i in range(internal_degree): for i in range(internal_degree):
local_dos[i2, i1] = local_dos[i2, i1] - np.imag(green_ii_n_minus[i2*internal_degree+i, i2*internal_degree+i])/pi local_dos[i2, i1] = local_dos[i2, i1] - np.imag(green_ii_n_minus[i2*internal_degree+i, i2*internal_degree+i])/math.pi
return local_dos return local_dos
def local_density_of_states_for_cubic_lattice_using_dyson_equation(fermi_energy, h00, h01, N3, N2, N1, internal_degree=1, broadening=0.01): def local_density_of_states_for_cubic_lattice_using_dyson_equation(fermi_energy, h00, h01, N3, N2, N1, internal_degree=1, broadening=0.01):
@ -863,7 +863,7 @@ def local_density_of_states_for_cubic_lattice_using_dyson_equation(fermi_energy,
for i2 in range(N2): for i2 in range(N2):
for i3 in range(N3): for i3 in range(N3):
for i in range(internal_degree): for i in range(internal_degree):
local_dos[i3, i2, i1] = local_dos[i3, i2, i1] -np.imag(green_ii_n_minus[i2*N3*internal_degree+i3*internal_degree+i, i2*N3*internal_degree+i3*internal_degree+i])/pi local_dos[i3, i2, i1] = local_dos[i3, i2, i1] -np.imag(green_ii_n_minus[i2*N3*internal_degree+i3*internal_degree+i, i2*N3*internal_degree+i3*internal_degree+i])/math.pi
return local_dos return local_dos
def local_density_of_states_for_square_lattice_with_self_energy_using_dyson_equation(fermi_energy, h00, h01, N2, N1, right_self_energy, left_self_energy, internal_degree=1, broadening=0.01): def local_density_of_states_for_square_lattice_with_self_energy_using_dyson_equation(fermi_energy, h00, h01, N2, N1, right_self_energy, left_self_energy, internal_degree=1, broadening=0.01):
@ -899,7 +899,7 @@ def local_density_of_states_for_square_lattice_with_self_energy_using_dyson_equa
green_ni_n_minus = green_ni_n green_ni_n_minus = green_ni_n
for i2 in range(N2): for i2 in range(N2):
for i in range(internal_degree): for i in range(internal_degree):
local_dos[i2, i1] = local_dos[i2, i1] - np.imag(green_ii_n_minus[i2*internal_degree+i, i2*internal_degree+i])/pi local_dos[i2, i1] = local_dos[i2, i1] - np.imag(green_ii_n_minus[i2*internal_degree+i, i2*internal_degree+i])/math.pi
return local_dos return local_dos
@ -1114,7 +1114,7 @@ def get_k_and_velocity_of_channel(fermi_energy, h00, h01):
for dim0 in range(dim): for dim0 in range(dim):
factor = factor+np.square(np.abs(temp[dim0, :])) factor = factor+np.square(np.abs(temp[dim0, :]))
for dim0 in range(2*dim): for dim0 in range(2*dim):
temp[:, dim0] = temp[:, dim0]/np.sqrt(factor[dim0]) temp[:, dim0] = temp[:, dim0]/math.sqrt(factor[dim0])
velocity_of_channel = np.zeros((2*dim), dtype=complex) velocity_of_channel = np.zeros((2*dim), dtype=complex)
for dim0 in range(2*dim): for dim0 in range(2*dim):
velocity_of_channel[dim0] = eigenvalue[dim0]*np.dot(np.dot(temp[0:dim, :].transpose().conj(), h01),temp[0:dim, :])[dim0, dim0] velocity_of_channel[dim0] = eigenvalue[dim0]*np.dot(np.dot(temp[0:dim, :].transpose().conj(), h01),temp[0:dim, :])[dim0, dim0]
@ -1200,8 +1200,8 @@ def calculate_scattering_matrix(fermi_energy, h00, h01, length=100):
for dim1 in range(dim): for dim1 in range(dim):
if_active = if_active_channel(k_right[dim0])*if_active_channel(k_right[dim1]) if_active = if_active_channel(k_right[dim0])*if_active_channel(k_right[dim1])
if if_active == 1: if if_active == 1:
transmission_matrix[dim0, dim1] = np.sqrt(np.abs(velocity_right[dim0]/velocity_right[dim1])) * transmission_matrix[dim0, dim1] transmission_matrix[dim0, dim1] = math.sqrt(np.abs(velocity_right[dim0]/velocity_right[dim1])) * transmission_matrix[dim0, dim1]
reflection_matrix[dim0, dim1] = np.sqrt(np.abs(velocity_left[dim0]/velocity_right[dim1]))*reflection_matrix[dim0, dim1] reflection_matrix[dim0, dim1] = math.sqrt(np.abs(velocity_left[dim0]/velocity_right[dim1]))*reflection_matrix[dim0, dim1]
else: else:
transmission_matrix[dim0, dim1] = 0 transmission_matrix[dim0, dim1] = 0
reflection_matrix[dim0, dim1] = 0 reflection_matrix[dim0, dim1] = 0
@ -1275,12 +1275,12 @@ def calculate_chern_number_for_square_lattice(hamiltonian_function, precision=10
dim = 1 dim = 1
else: else:
dim = np.array(hamiltonian_function(0, 0)).shape[0] dim = np.array(hamiltonian_function(0, 0)).shape[0]
delta = 2*pi/precision delta = 2*math.pi/precision
chern_number = np.zeros(dim, dtype=complex) chern_number = np.zeros(dim, dtype=complex)
for kx in np.arange(-pi, pi, delta): for kx in np.arange(-math.pi, math.pi, delta):
if print_show == 1: if print_show == 1:
print(kx) print(kx)
for ky in np.arange(-pi, pi, delta): for ky in np.arange(-math.pi, math.pi, delta):
H = hamiltonian_function(kx, ky) H = hamiltonian_function(kx, ky)
vector = guan.calculate_eigenvector(H) vector = guan.calculate_eigenvector(H)
H_delta_kx = hamiltonian_function(kx+delta, ky) H_delta_kx = hamiltonian_function(kx+delta, ky)
@ -1300,16 +1300,16 @@ def calculate_chern_number_for_square_lattice(hamiltonian_function, precision=10
Uy_x = np.dot(np.conj(vector_delta_kx_i), vector_delta_kx_ky_i)/abs(np.dot(np.conj(vector_delta_kx_i), vector_delta_kx_ky_i)) Uy_x = np.dot(np.conj(vector_delta_kx_i), vector_delta_kx_ky_i)/abs(np.dot(np.conj(vector_delta_kx_i), vector_delta_kx_ky_i))
F = cmath.log(Ux*Uy_x*(1/Ux_y)*(1/Uy)) F = cmath.log(Ux*Uy_x*(1/Ux_y)*(1/Uy))
chern_number[i] = chern_number[i] + F chern_number[i] = chern_number[i] + F
chern_number = chern_number/(2*pi*1j) chern_number = chern_number/(2*math.pi*1j)
return chern_number return chern_number
def calculate_chern_number_for_square_lattice_with_Wilson_loop(hamiltonian_function, precision_of_plaquettes=10, precision_of_Wilson_loop=100, print_show=0): def calculate_chern_number_for_square_lattice_with_Wilson_loop(hamiltonian_function, precision_of_plaquettes=10, precision_of_Wilson_loop=100, print_show=0):
delta = 2*pi/precision_of_plaquettes delta = 2*math.pi/precision_of_plaquettes
chern_number = 0 chern_number = 0
for kx in np.arange(-pi, pi, delta): for kx in np.arange(-math.pi, math.pi, delta):
if print_show == 1: if print_show == 1:
print(kx) print(kx)
for ky in np.arange(-pi, pi, delta): for ky in np.arange(-math.pi, math.pi, delta):
vector_array = [] vector_array = []
# line_1 # line_1
for i0 in range(precision_of_Wilson_loop+1): for i0 in range(precision_of_Wilson_loop+1):
@ -1341,7 +1341,7 @@ def calculate_chern_number_for_square_lattice_with_Wilson_loop(hamiltonian_funct
Wilson_loop = Wilson_loop*np.dot(vector_array[len(vector_array)-1].transpose().conj(), vector_array[0]) Wilson_loop = Wilson_loop*np.dot(vector_array[len(vector_array)-1].transpose().conj(), vector_array[0])
arg = np.log(np.diagonal(Wilson_loop))/1j arg = np.log(np.diagonal(Wilson_loop))/1j
chern_number = chern_number + arg chern_number = chern_number + arg
chern_number = chern_number/(2*pi) chern_number = chern_number/(2*math.pi)
return chern_number return chern_number
def calculate_chern_number_for_honeycomb_lattice(hamiltonian_function, a=1, precision=300, print_show=0): def calculate_chern_number_for_honeycomb_lattice(hamiltonian_function, a=1, precision=300, print_show=0):
@ -1350,16 +1350,16 @@ def calculate_chern_number_for_honeycomb_lattice(hamiltonian_function, a=1, prec
else: else:
dim = np.array(hamiltonian_function(0, 0)).shape[0] dim = np.array(hamiltonian_function(0, 0)).shape[0]
chern_number = np.zeros(dim, dtype=complex) chern_number = np.zeros(dim, dtype=complex)
L1 = 4*sqrt(3)*pi/9/a L1 = 4*math.sqrt(3)*math.pi/9/a
L2 = 2*sqrt(3)*pi/9/a L2 = 2*math.sqrt(3)*math.pi/9/a
L3 = 2*pi/3/a L3 = 2*math.pi/3/a
delta1 = 2*L1/precision delta1 = 2*L1/precision
delta3 = 2*L3/precision delta3 = 2*L3/precision
for kx in np.arange(-L1, L1, delta1): for kx in np.arange(-L1, L1, delta1):
if print_show == 1: if print_show == 1:
print(kx) print(kx)
for ky in np.arange(-L3, L3, delta3): for ky in np.arange(-L3, L3, delta3):
if (-L2<=kx<=L2) or (kx>L2 and -(L1-kx)*tan(pi/3)<=ky<=(L1-kx)*tan(pi/3)) or (kx<-L2 and -(kx-(-L1))*tan(pi/3)<=ky<=(kx-(-L1))*tan(pi/3)): if (-L2<=kx<=L2) or (kx>L2 and -(L1-kx)*math.tan(math.pi/3)<=ky<=(L1-kx)*math.tan(math.pi/3)) or (kx<-L2 and -(kx-(-L1))*math.tan(math.pi/3)<=ky<=(kx-(-L1))*math.tan(math.pi/3)):
H = hamiltonian_function(kx, ky) H = hamiltonian_function(kx, ky)
vector = guan.calculate_eigenvector(H) vector = guan.calculate_eigenvector(H)
H_delta_kx = hamiltonian_function(kx+delta1, ky) H_delta_kx = hamiltonian_function(kx+delta1, ky)
@ -1379,10 +1379,10 @@ def calculate_chern_number_for_honeycomb_lattice(hamiltonian_function, a=1, prec
Uy_x = np.dot(np.conj(vector_delta_kx_i), vector_delta_kx_ky_i)/abs(np.dot(np.conj(vector_delta_kx_i), vector_delta_kx_ky_i)) Uy_x = np.dot(np.conj(vector_delta_kx_i), vector_delta_kx_ky_i)/abs(np.dot(np.conj(vector_delta_kx_i), vector_delta_kx_ky_i))
F = cmath.log(Ux*Uy_x*(1/Ux_y)*(1/Uy)) F = cmath.log(Ux*Uy_x*(1/Ux_y)*(1/Uy))
chern_number[i] = chern_number[i] + F chern_number[i] = chern_number[i] + F
chern_number = chern_number/(2*pi*1j) chern_number = chern_number/(2*math.pi*1j)
return chern_number return chern_number
def calculate_wilson_loop(hamiltonian_function, k_min=-pi, k_max=pi, precision=100, print_show=0): def calculate_wilson_loop(hamiltonian_function, k_min=-math.pi, k_max=math.pi, precision=100, print_show=0):
k_array = np.linspace(k_min, k_max, precision) k_array = np.linspace(k_min, k_max, precision)
dim = np.array(hamiltonian_function(0)).shape[0] dim = np.array(hamiltonian_function(0)).shape[0]
wilson_loop_array = np.ones(dim, dtype=complex) wilson_loop_array = np.ones(dim, dtype=complex)