From 055e9a7724a4f7d9479313293110dc2de7bce417 Mon Sep 17 00:00:00 2001 From: guanjihuan Date: Mon, 10 Oct 2022 03:05:12 +0800 Subject: [PATCH] 0.0.143 --- PyPI/setup.cfg | 2 +- PyPI/src/guan.egg-info/PKG-INFO | 2 +- PyPI/src/guan/__init__.py | 18 +++++++++--------- 3 files changed, 11 insertions(+), 11 deletions(-) diff --git a/PyPI/setup.cfg b/PyPI/setup.cfg index d6f757e..e28d01b 100644 --- a/PyPI/setup.cfg +++ b/PyPI/setup.cfg @@ -1,7 +1,7 @@ [metadata] # replace with your username: name = guan -version = 0.0.142 +version = 0.0.143 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 7bd5028..686cc41 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: 0.0.142 +Version: 0.0.143 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 c6734c9..c773b33 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-0.0.142, updated on December 10, 2022. +# The current version is guan-0.0.143, updated on December 10, 2022. # 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) 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))