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0.0.143

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guanjihuan
2022-10-10 03:05:12 +08:00
parent 46d5e23f06
commit 055e9a7724
3 changed files with 11 additions and 11 deletions
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2
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 = 0.0.142 version = 0.0.143
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

2
PyPI/src/guan.egg-info/PKG-INFO
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@@ -1,6 +1,6 @@
Metadata-Version: 2.1 Metadata-Version: 2.1
Name: guan Name: guan
Version: 0.0.142 Version: 0.0.143
Summary: An open source python package Summary: An open source python package
Home-page: https://py.guanjihuan.com Home-page: https://py.guanjihuan.com
Author: guanjihuan Author: guanjihuan

18
PyPI/src/guan/__init__.py
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@@ -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. # 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-0.0.142, updated on December 10, 2022. # The current version is guan-0.0.143, updated on December 10, 2022.
# Installation: pip install --upgrade guan # Installation: pip install --upgrade guan
@@ -1569,46 +1569,46 @@ def calculate_chern_number_for_square_lattice_with_efficient_method_for_degenera
eigenvalue, vector_delta_kx_ky = np.linalg.eigh(H_delta_kx_ky) eigenvalue, vector_delta_kx_ky = np.linalg.eigh(H_delta_kx_ky)
dim = len(index_of_bands) dim = len(index_of_bands)
det_value = 1 det_value = 1
# first dot # first dot product
dot_matrix = np.zeros((dim , dim), dtype=complex) dot_matrix = np.zeros((dim , dim), dtype=complex)
i0 = 0 i0 = 0
for dim1 in index_of_bands: for dim1 in index_of_bands:
j0 = 0 j0 = 0
for dim2 in index_of_bands: 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 j0 += 1
i0 += 1 i0 += 1
dot_matrix = np.linalg.det(dot_matrix)/abs(np.linalg.det(dot_matrix)) dot_matrix = np.linalg.det(dot_matrix)/abs(np.linalg.det(dot_matrix))
det_value = det_value*dot_matrix det_value = det_value*dot_matrix
# second dot # second dot product
dot_matrix = np.zeros((dim , dim), dtype=complex) dot_matrix = np.zeros((dim , dim), dtype=complex)
i0 = 0 i0 = 0
for dim1 in index_of_bands: for dim1 in index_of_bands:
j0 = 0 j0 = 0
for dim2 in index_of_bands: 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 j0 += 1
i0 += 1 i0 += 1
dot_matrix = np.linalg.det(dot_matrix)/abs(np.linalg.det(dot_matrix)) dot_matrix = np.linalg.det(dot_matrix)/abs(np.linalg.det(dot_matrix))
det_value = det_value*dot_matrix det_value = det_value*dot_matrix
# third dot # third dot product
dot_matrix = np.zeros((dim , dim), dtype=complex) dot_matrix = np.zeros((dim , dim), dtype=complex)
i0 = 0 i0 = 0
for dim1 in index_of_bands: for dim1 in index_of_bands:
j0 = 0 j0 = 0
for dim2 in index_of_bands: 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 j0 += 1
i0 += 1 i0 += 1
dot_matrix = np.linalg.det(dot_matrix)/abs(np.linalg.det(dot_matrix)) dot_matrix = np.linalg.det(dot_matrix)/abs(np.linalg.det(dot_matrix))
det_value = det_value*dot_matrix det_value = det_value*dot_matrix
# four dot # four dot product
dot_matrix = np.zeros((dim , dim), dtype=complex) dot_matrix = np.zeros((dim , dim), dtype=complex)
i0 = 0 i0 = 0
for dim1 in index_of_bands: for dim1 in index_of_bands:
j0 = 0 j0 = 0
for dim2 in index_of_bands: 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 j0 += 1
i0 += 1 i0 += 1
dot_matrix = np.linalg.det(dot_matrix)/abs(np.linalg.det(dot_matrix)) dot_matrix = np.linalg.det(dot_matrix)/abs(np.linalg.det(dot_matrix))