0.0.126

API_Reference.py

@@ -246,6 +246,8 @@ guan.print_or_write_scattering_matrix(fermi_energy, h00, h01, length=100, print_
 
 chern_number = guan.calculate_chern_number_for_square_lattice_with_efficient_method(hamiltonian_function, precision=100, print_show=0)
 
+chern_number = guan.calculate_chern_number_for_square_lattice_with_efficient_method_for_degenerate_case(hamiltonian_function, index_of_bands=[0, 1], precision=100, print_show=0)
+
 chern_number = guan.calculate_chern_number_for_square_lattice_with_wilson_loop(hamiltonian_function, precision_of_plaquettes=20, precision_of_wilson_loop=5, print_show=0)
 
 chern_number = guan.calculate_chern_number_for_square_lattice_with_wilson_loop_for_degenerate_case(hamiltonian_function, index_of_bands=[0, 1], precision_of_plaquettes=20, precision_of_wilson_loop=5, print_show=0)

PyPI/setup.cfg

@@ -1,7 +1,7 @@
 [metadata]
 # replace with your username:
 name = guan
-version = 0.0.125
+version = 0.0.126
 author = guanjihuan
 author_email = guanjihuan@163.com
 description = An open source python package

PyPI/src/guan.egg-info/PKG-INFO

@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: guan
-Version: 0.0.125
+Version: 0.0.126
 Summary: An open source python package
 Home-page: https://py.guanjihuan.com
 Author: guanjihuan

PyPI/src/guan/__init__.py

@@ -2,7 +2,7 @@
 
 # With this package, you can calculate band structures, density of states, quantum transport and topological invariant of tight-binding models by invoking the functions you need. Other frequently used functions are also integrated in this package, such as file reading/writing, figure plotting, data processing.
 
-# The current version is guan-0.0.125, updated on August 25, 2022.
+# The current version is guan-0.0.126, updated on August 28, 2022.
 
 # Installation: pip install --upgrade guan
 
@@ -1551,6 +1551,71 @@ def calculate_chern_number_for_square_lattice_with_efficient_method(hamiltonian_
     chern_number = chern_number/(2*math.pi*1j)
     return chern_number
 
+def calculate_chern_number_for_square_lattice_with_efficient_method_for_degenerate_case(hamiltonian_function, index_of_bands=[0, 1], precision=100, print_show=0): 
+    delta = 2*math.pi/precision
+    chern_number = 0
+    for kx in np.arange(-math.pi, math.pi, delta):
+        if print_show == 1:
+            print(kx)
+        for ky in np.arange(-math.pi, math.pi, delta):
+            H = hamiltonian_function(kx, ky)
+            eigenvalue, vector = np.linalg.eigh(H) 
+            H_delta_kx = hamiltonian_function(kx+delta, ky) 
+            eigenvalue, vector_delta_kx = np.linalg.eigh(H_delta_kx) 
+            H_delta_ky = hamiltonian_function(kx, ky+delta)
+            eigenvalue, vector_delta_ky = np.linalg.eigh(H_delta_ky) 
+            H_delta_kx_ky = hamiltonian_function(kx+delta, ky+delta)
+            eigenvalue, vector_delta_kx_ky = np.linalg.eigh(H_delta_kx_ky)
+            dim = len(index_of_bands)
+            det_value = 1
+            # first dot
+            dot_matrix = np.zeros((dim , dim), dtype=complex)
+            i0 = 0
+            for dim1 in index_of_bands:
+                j0 = 0
+                for dim2 in index_of_bands:
+                    dot_matrix[dim1, dim2] = np.dot(np.conj(vector[:, dim1]), vector_delta_kx[:, dim2])
+                    j0 += 1
+                i0 += 1
+            dot_matrix = np.linalg.det(dot_matrix)/abs(np.linalg.det(dot_matrix))
+            det_value = det_value*dot_matrix
+            # second dot
+            dot_matrix = np.zeros((dim , dim), dtype=complex)
+            i0 = 0
+            for dim1 in index_of_bands:
+                j0 = 0
+                for dim2 in index_of_bands:
+                    dot_matrix[dim1, dim2] = np.dot(np.conj(vector_delta_kx[:, dim1]), vector_delta_kx_ky[:, dim2])
+                    j0 += 1
+                i0 += 1
+            dot_matrix = np.linalg.det(dot_matrix)/abs(np.linalg.det(dot_matrix))
+            det_value = det_value*dot_matrix
+            # third dot
+            dot_matrix = np.zeros((dim , dim), dtype=complex)
+            i0 = 0
+            for dim1 in index_of_bands:
+                j0 = 0
+                for dim2 in index_of_bands:
+                    dot_matrix[dim1, dim2] = np.dot(np.conj(vector_delta_kx_ky[:, dim1]), vector_delta_ky[:, dim2])
+                    j0 += 1
+                i0 += 1
+            dot_matrix = np.linalg.det(dot_matrix)/abs(np.linalg.det(dot_matrix))
+            det_value = det_value*dot_matrix
+            # four dot
+            dot_matrix = np.zeros((dim , dim), dtype=complex)
+            i0 = 0
+            for dim1 in index_of_bands:
+                j0 = 0
+                for dim2 in index_of_bands:
+                    dot_matrix[dim1, dim2] = np.dot(np.conj(vector_delta_ky[:, dim1]), vector[:, dim2])
+                    j0 += 1
+                i0 += 1
+            dot_matrix = np.linalg.det(dot_matrix)/abs(np.linalg.det(dot_matrix))
+            det_value= det_value*dot_matrix
+            chern_number += cmath.log(det_value)
+    chern_number = chern_number/(2*math.pi*1j)
+    return chern_number
+
 def calculate_chern_number_for_square_lattice_with_wilson_loop(hamiltonian_function, precision_of_plaquettes=20, precision_of_wilson_loop=5, print_show=0):
     delta = 2*math.pi/precision_of_plaquettes
     chern_number = 0