diff --git a/API_Reference.py b/API_Reference.py index 86ba14b..127205c 100644 --- a/API_Reference.py +++ b/API_Reference.py @@ -254,6 +254,8 @@ chern_number = guan.calculate_chern_number_for_square_lattice_with_wilson_loop_f k_array, berry_curvature_array = guan.calculate_berry_curvature_with_efficient_method(hamiltonian_function, k_min=-math.pi, k_max=math.pi, precision=100, print_show=0) +k_array, berry_curvature_array = guan.calculate_berry_curvature_with_efficient_method_for_degenerate_case(hamiltonian_function, index_of_bands=[0, 1], k_min=-math.pi, k_max=math.pi, precision=100, print_show=0) + k_array, berry_curvature_array = guan.calculate_berry_curvature_with_wilson_loop(hamiltonian_function, k_min=-math.pi, k_max=math.pi, precision_of_plaquettes=20, precision_of_wilson_loop=5, print_show=0) k_array, berry_curvature_array = guan.calculate_berry_curvature_with_wilson_loop_for_degenerate_case(hamiltonian_function, index_of_bands=[0, 1], k_min=-math.pi, k_max=math.pi, precision_of_plaquettes=20, precision_of_wilson_loop=5, print_show=0) diff --git a/PyPI/setup.cfg b/PyPI/setup.cfg index f6dd3c7..e6fe788 100644 --- a/PyPI/setup.cfg +++ b/PyPI/setup.cfg @@ -1,7 +1,7 @@ [metadata] # replace with your username: name = guan -version = 0.0.126 +version = 0.0.127 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 01a7503..d4a9369 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: 0.0.126 +Version: 0.0.127 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 2a4cd30..9bf8889 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-0.0.126, updated on August 28, 2022. +# The current version is guan-0.0.127, updated on August 28, 2022. # Installation: pip install --upgrade guan @@ -1755,6 +1755,76 @@ def calculate_berry_curvature_with_efficient_method(hamiltonian_function, k_min= i0 += 1 return k_array, berry_curvature_array +def calculate_berry_curvature_with_efficient_method_for_degenerate_case(hamiltonian_function, index_of_bands=[0, 1], k_min=-math.pi, k_max=math.pi, precision=100, print_show=0): + delta = (k_max-k_min)/precision + k_array = np.arange(k_min, k_max, delta) + berry_curvature_array = np.zeros((k_array.shape[0], k_array.shape[0]), dtype=complex) + i00 = 0 + for kx in np.arange(k_min, k_max, delta): + if print_show == 1: + print(kx) + j00 = 0 + for ky in np.arange(k_min, k_max, 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 + berry_curvature = cmath.log(det_value)/delta/delta*1j + berry_curvature_array[j00, i00] = berry_curvature + j00 += 1 + i00 += 1 + return k_array, berry_curvature_array + def calculate_berry_curvature_with_wilson_loop(hamiltonian_function, k_min=-math.pi, k_max=math.pi, precision_of_plaquettes=20, precision_of_wilson_loop=5, print_show=0): if np.array(hamiltonian_function(0, 0)).shape==(): dim = 1