version 0.0.22

API_reference.py

@@ -89,10 +89,8 @@ k_right, k_left, velocity_right, velocity_left, f_right, f_left, u_right, u_left
 transmission_matrix, reflection_matrix, k_right, k_left, velocity_right, velocity_left, ind_right_active = guan.calculate_scattering_matrix(fermi_energy, h00, h01, length=100)
 guan.print_or_write_scattering_matrix(fermi_energy, h00, h01, length=100, on_print=1, on_write=0)
 
-# calculate Chern number    # Source code: https://py.guanjihuan.com/calculate_chern_number
+# calculate topological invariant     # Source code: https://py.guanjihuan.com/source-code/calculate_topological_invariant
 chern_number = guan.calculate_chern_number_for_square_lattice(hamiltonian_function, precision=100)
-
-# calculate Wilson loop    # Source code: https://py.guanjihuan.com/calculate_wilson_loop
 wilson_loop_array = guan.calculate_wilson_loop(hamiltonian_function, k_min=-pi, k_max=pi, precision=100)
 
 # read and write    # Source code: https://py.guanjihuan.com/read_and_write

PyPI/setup.cfg

@@ -1,7 +1,7 @@
 [metadata]
 # replace with your username:
 name = guan
-version = 0.0.21
+version = 0.0.22
 author = guanjihuan
 author_email = guanjihuan@163.com
 description = An open source python package

PyPI/src/guan/__init__.py

@@ -11,8 +11,7 @@ from .calculate_Green_functions import *
 from .calculate_density_of_states import *
 from .calculate_conductance import *
 from .calculate_scattering_matrix import *
-from .calculate_Chern_number import *
+from .calculate_topological_invariant import *
-from .calculate_Wilson_loop import *
 from .read_and_write import *
 from .plot_figures import *
 from .others import *

PyPI/src/guan/calculate_Wilson_loop.py

@@ -1,24 +0,0 @@
-# Guan is an open-source python package developed and maintained by https://www.guanjihuan.com. The primary location of this package is on website https://py.guanjihuan.com.
-
-# calculate Wilson loop
-
-import numpy as np
-from math import *
-from .calculate_wave_functions import *
-
-def calculate_wilson_loop(hamiltonian_function, k_min=-pi, k_max=pi, precision=100):
-    k_array = np.linspace(k_min, k_max, precision)
-    dim = np.array(hamiltonian_function(0)).shape[0]
-    wilson_loop_array = np.ones(dim, dtype=complex)
-    for i in range(dim):
-        eigenvector_array = []
-        for k in k_array:
-            eigenvector  = calculate_eigenvector(hamiltonian_function(k))  
-            if k != k_max:
-                eigenvector_array.append(eigenvector[:, i])
-            else:
-                eigenvector_array.append(eigenvector_array[0])
-        for i0 in range(precision-1):
-            F = np.dot(eigenvector_array[i0+1].transpose().conj(), eigenvector_array[i0])
-            wilson_loop_array[i] = np.dot(F, wilson_loop_array[i])
-    return wilson_loop_array

PyPI/src/guan/calculate_topological_invariant.py

@@ -1,11 +1,11 @@
 # Guan is an open-source python package developed and maintained by https://www.guanjihuan.com. The primary location of this package is on website https://py.guanjihuan.com.
 
-# calculate Chern number
+# calculate topological invariant
 
 import numpy as np
 import cmath
 from math import *
-from .calculate_wave_functions import *
+from .calculate_band_structures_and_wave_functions import *
 
 def calculate_chern_number_for_square_lattice(hamiltonian_function, precision=100):
     if np.array(hamiltonian_function(0, 0)).shape==():
@@ -37,3 +37,20 @@ def calculate_chern_number_for_square_lattice(hamiltonian_function, precision=10
                 chern_number[i] = chern_number[i] + F
     chern_number = chern_number/(2*pi*1j)
     return chern_number
+
+def calculate_wilson_loop(hamiltonian_function, k_min=-pi, k_max=pi, precision=100):
+    k_array = np.linspace(k_min, k_max, precision)
+    dim = np.array(hamiltonian_function(0)).shape[0]
+    wilson_loop_array = np.ones(dim, dtype=complex)
+    for i in range(dim):
+        eigenvector_array = []
+        for k in k_array:
+            eigenvector  = calculate_eigenvector(hamiltonian_function(k))  
+            if k != k_max:
+                eigenvector_array.append(eigenvector[:, i])
+            else:
+                eigenvector_array.append(eigenvector_array[0])
+        for i0 in range(precision-1):
+            F = np.dot(eigenvector_array[i0+1].transpose().conj(), eigenvector_array[i0])
+            wilson_loop_array[i] = np.dot(F, wilson_loop_array[i])
+    return wilson_loop_array