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Update calculation_of_Chern_number_by_efficient_method_for_degenerate_case.py

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guanjihuan 2022-10-10 03:01:14 +08:00
parent e97abfe16b
commit 4fe2a4a017
1 changed files with 8 additions and 8 deletions
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academic_codes/2022.08.28_calculation_of_Chern_number_by_efficient_method_for_degenerate_case/calculation_of_Chern_number_by_efficient_method_for_degenerate_case.py
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@ -63,46 +63,46 @@ 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))