0.0.121
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@ -248,7 +248,7 @@ chern_number = guan.calculate_chern_number_for_square_lattice(hamiltonian_functi
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chern_number = guan.calculate_chern_number_for_square_lattice_with_Wilson_loop(hamiltonian_function, precision_of_plaquettes=20, precision_of_Wilson_loop=5, print_show=0)
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chern_number = guan.calculate_chern_number_for_square_lattice_with_Wilson_loop_for_degenerate_case(hamiltonian_function, num_of_bands=[0, 1], precision_of_plaquettes=20, precision_of_Wilson_loop=5, print_show=0)
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chern_number = guan.calculate_chern_number_for_square_lattice_with_Wilson_loop_for_degenerate_case(hamiltonian_function, index_of_bands=[0, 1], precision_of_plaquettes=20, precision_of_Wilson_loop=5, print_show=0)
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chern_number = guan.calculate_chern_number_for_honeycomb_lattice(hamiltonian_function, a=1, precision=300, print_show=0)
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@ -1,7 +1,7 @@
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[metadata]
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# replace with your username:
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name = guan
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version = 0.0.120
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version = 0.0.121
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author = guanjihuan
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author_email = guanjihuan@163.com
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description = An open source python package
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@ -1,6 +1,6 @@
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Metadata-Version: 2.1
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Name: guan
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Version: 0.0.120
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Version: 0.0.121
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Summary: An open source python package
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Home-page: https://py.guanjihuan.com
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Author: guanjihuan
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@ -2,7 +2,7 @@
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# 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.
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# The current version is guan-0.0.120, updated on August 12, 2022.
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# The current version is guan-0.0.121, updated on August 12, 2022.
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# Installation: pip install --upgrade guan
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@ -1592,7 +1592,7 @@ def calculate_chern_number_for_square_lattice_with_Wilson_loop(hamiltonian_funct
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chern_number = chern_number/(2*math.pi)
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return chern_number
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def calculate_chern_number_for_square_lattice_with_Wilson_loop_for_degenerate_case(hamiltonian_function, num_of_bands=[0, 1], precision_of_plaquettes=20, precision_of_Wilson_loop=5, print_show=0):
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def calculate_chern_number_for_square_lattice_with_Wilson_loop_for_degenerate_case(hamiltonian_function, index_of_bands=[0, 1], precision_of_plaquettes=20, precision_of_Wilson_loop=5, print_show=0):
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delta = 2*math.pi/precision_of_plaquettes
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chern_number = 0
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for kx in np.arange(-math.pi, math.pi, delta):
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@ -1625,13 +1625,13 @@ def calculate_chern_number_for_square_lattice_with_Wilson_loop_for_degenerate_ca
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vector_delta = eigenvector[:, np.argsort(np.real(eigenvalue))]
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vector_array.append(vector_delta)
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Wilson_loop = 1
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dim = len(num_of_bands)
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dim = len(index_of_bands)
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for i0 in range(len(vector_array)-1):
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dot_matrix = np.zeros((dim , dim), dtype=complex)
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i01 = 0
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for dim1 in num_of_bands:
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for dim1 in index_of_bands:
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i02 = 0
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for dim2 in num_of_bands:
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for dim2 in index_of_bands:
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dot_matrix[i01, i02] = np.dot(vector_array[i0][:, dim1].transpose().conj(), vector_array[i0+1][:, dim2])
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i02 += 1
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i01 += 1
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@ -1639,9 +1639,9 @@ def calculate_chern_number_for_square_lattice_with_Wilson_loop_for_degenerate_ca
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Wilson_loop = Wilson_loop*det_value
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dot_matrix_plus = np.zeros((dim , dim), dtype=complex)
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i01 = 0
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for dim1 in num_of_bands:
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for dim1 in index_of_bands:
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i02 = 0
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for dim2 in num_of_bands:
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for dim2 in index_of_bands:
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dot_matrix_plus[i01, i02] = np.dot(vector_array[len(vector_array)-1][:, dim1].transpose().conj(), vector_array[0][:, dim2])
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i02 += 1
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i01 += 1
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