diff --git a/PyPI/setup.cfg b/PyPI/setup.cfg
index bad4809..d6f757e 100644
--- a/PyPI/setup.cfg
+++ b/PyPI/setup.cfg
@@ -1,7 +1,7 @@
 [metadata]
 # replace with your username:
 name = guan
-version = 0.0.141
+version = 0.0.142
 author = guanjihuan
 author_email = guanjihuan@163.com
 description = An open source python package
diff --git a/PyPI/src/guan.egg-info/PKG-INFO b/PyPI/src/guan.egg-info/PKG-INFO
index 9f4ec07..7bd5028 100644
--- a/PyPI/src/guan.egg-info/PKG-INFO
+++ b/PyPI/src/guan.egg-info/PKG-INFO
@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: guan
-Version: 0.0.141
+Version: 0.0.142
 Summary: An open source python package
 Home-page: https://py.guanjihuan.com
 Author: guanjihuan
diff --git a/PyPI/src/guan/__init__.py b/PyPI/src/guan/__init__.py
index f791829..c6734c9 100644
--- a/PyPI/src/guan/__init__.py
+++ b/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-0.0.141, updated on December 09, 2022.
+# The current version is guan-0.0.142, updated on December 10, 2022.
 
 # Installation: pip install --upgrade guan
 
@@ -1776,46 +1776,46 @@ def calculate_berry_curvature_with_efficient_method_for_degenerate_case(hamilton
             eigenvalue, vector_delta_kx_ky = np.linalg.eigh(H_delta_kx_ky)
             dim = len(index_of_bands)
             det_value = 1
-            # first dot
+            # first dot product
             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])
+                    dot_matrix[i0, j0] = 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
+            # second dot product
             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])
+                    dot_matrix[i0, j0] = 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
+            # third dot product
             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])
+                    dot_matrix[i0, j0] = 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
+            # four dot product
             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])
+                    dot_matrix[i0, j0] = 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))