diff --git a/API_Reference.py b/API_Reference.py index 99079f6..df48171 100644 --- a/API_Reference.py +++ b/API_Reference.py @@ -246,9 +246,9 @@ guan.print_or_write_scattering_matrix(fermi_energy, h00, h01, length=100, print_ chern_number = guan.calculate_chern_number_for_square_lattice(hamiltonian_function, precision=100, print_show=0) -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) +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) -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) +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) chern_number = guan.calculate_chern_number_for_honeycomb_lattice(hamiltonian_function, a=1, precision=300, print_show=0) diff --git a/PyPI/setup.cfg b/PyPI/setup.cfg index e7b5d64..7cae682 100644 --- a/PyPI/setup.cfg +++ b/PyPI/setup.cfg @@ -1,7 +1,7 @@ [metadata] # replace with your username: name = guan -version = 0.0.121 +version = 0.0.122 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 fa3cdbe..bd3c597 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: 0.0.121 +Version: 0.0.122 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 289689f..bd116e2 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-0.0.121, updated on August 12, 2022. +# The current version is guan-0.0.122, updated on August 13, 2022. # Installation: pip install --upgrade guan @@ -1551,7 +1551,7 @@ def calculate_chern_number_for_square_lattice(hamiltonian_function, precision=10 chern_number = chern_number/(2*math.pi*1j) return chern_number -def calculate_chern_number_for_square_lattice_with_Wilson_loop(hamiltonian_function, precision_of_plaquettes=20, precision_of_Wilson_loop=5, print_show=0): +def calculate_chern_number_for_square_lattice_with_wilson_loop(hamiltonian_function, precision_of_plaquettes=20, precision_of_wilson_loop=5, print_show=0): delta = 2*math.pi/precision_of_plaquettes chern_number = 0 for kx in np.arange(-math.pi, math.pi, delta): @@ -1560,39 +1560,39 @@ def calculate_chern_number_for_square_lattice_with_Wilson_loop(hamiltonian_funct for ky in np.arange(-math.pi, math.pi, delta): vector_array = [] # line_1 - for i0 in range(precision_of_Wilson_loop): - H_delta = hamiltonian_function(kx+delta/precision_of_Wilson_loop*i0, ky) + for i0 in range(precision_of_wilson_loop): + H_delta = hamiltonian_function(kx+delta/precision_of_wilson_loop*i0, ky) eigenvalue, eigenvector = np.linalg.eig(H_delta) vector_delta = eigenvector[:, np.argsort(np.real(eigenvalue))] vector_array.append(vector_delta) # line_2 - for i0 in range(precision_of_Wilson_loop): - H_delta = hamiltonian_function(kx+delta, ky+delta/precision_of_Wilson_loop*i0) + for i0 in range(precision_of_wilson_loop): + H_delta = hamiltonian_function(kx+delta, ky+delta/precision_of_wilson_loop*i0) eigenvalue, eigenvector = np.linalg.eig(H_delta) vector_delta = eigenvector[:, np.argsort(np.real(eigenvalue))] vector_array.append(vector_delta) # line_3 - for i0 in range(precision_of_Wilson_loop): - H_delta = hamiltonian_function(kx+delta-delta/precision_of_Wilson_loop*i0, ky+delta) + for i0 in range(precision_of_wilson_loop): + H_delta = hamiltonian_function(kx+delta-delta/precision_of_wilson_loop*i0, ky+delta) eigenvalue, eigenvector = np.linalg.eig(H_delta) vector_delta = eigenvector[:, np.argsort(np.real(eigenvalue))] vector_array.append(vector_delta) # line_4 - for i0 in range(precision_of_Wilson_loop): - H_delta = hamiltonian_function(kx, ky+delta-delta/precision_of_Wilson_loop*i0) + for i0 in range(precision_of_wilson_loop): + H_delta = hamiltonian_function(kx, ky+delta-delta/precision_of_wilson_loop*i0) eigenvalue, eigenvector = np.linalg.eig(H_delta) vector_delta = eigenvector[:, np.argsort(np.real(eigenvalue))] vector_array.append(vector_delta) - Wilson_loop = 1 + wilson_loop = 1 for i0 in range(len(vector_array)-1): - Wilson_loop = Wilson_loop*np.dot(vector_array[i0].transpose().conj(), vector_array[i0+1]) - Wilson_loop = Wilson_loop*np.dot(vector_array[len(vector_array)-1].transpose().conj(), vector_array[0]) - arg = np.log(np.diagonal(Wilson_loop))/1j + wilson_loop = wilson_loop*np.dot(vector_array[i0].transpose().conj(), vector_array[i0+1]) + wilson_loop = wilson_loop*np.dot(vector_array[len(vector_array)-1].transpose().conj(), vector_array[0]) + arg = np.log(np.diagonal(wilson_loop))/1j chern_number = chern_number + arg chern_number = chern_number/(2*math.pi) return chern_number -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): +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): delta = 2*math.pi/precision_of_plaquettes chern_number = 0 for kx in np.arange(-math.pi, math.pi, delta): @@ -1601,30 +1601,30 @@ def calculate_chern_number_for_square_lattice_with_Wilson_loop_for_degenerate_ca for ky in np.arange(-math.pi, math.pi, delta): vector_array = [] # line_1 - for i0 in range(precision_of_Wilson_loop): - H_delta = hamiltonian_function(kx+delta/precision_of_Wilson_loop*i0, ky) + for i0 in range(precision_of_wilson_loop): + H_delta = hamiltonian_function(kx+delta/precision_of_wilson_loop*i0, ky) eigenvalue, eigenvector = np.linalg.eig(H_delta) vector_delta = eigenvector[:, np.argsort(np.real(eigenvalue))] vector_array.append(vector_delta) # line_2 - for i0 in range(precision_of_Wilson_loop): - H_delta = hamiltonian_function(kx+delta, ky+delta/precision_of_Wilson_loop*i0) + for i0 in range(precision_of_wilson_loop): + H_delta = hamiltonian_function(kx+delta, ky+delta/precision_of_wilson_loop*i0) eigenvalue, eigenvector = np.linalg.eig(H_delta) vector_delta = eigenvector[:, np.argsort(np.real(eigenvalue))] vector_array.append(vector_delta) # line_3 - for i0 in range(precision_of_Wilson_loop): - H_delta = hamiltonian_function(kx+delta-delta/precision_of_Wilson_loop*i0, ky+delta) + for i0 in range(precision_of_wilson_loop): + H_delta = hamiltonian_function(kx+delta-delta/precision_of_wilson_loop*i0, ky+delta) eigenvalue, eigenvector = np.linalg.eig(H_delta) vector_delta = eigenvector[:, np.argsort(np.real(eigenvalue))] vector_array.append(vector_delta) # line_4 - for i0 in range(precision_of_Wilson_loop): - H_delta = hamiltonian_function(kx, ky+delta-delta/precision_of_Wilson_loop*i0) + for i0 in range(precision_of_wilson_loop): + H_delta = hamiltonian_function(kx, ky+delta-delta/precision_of_wilson_loop*i0) eigenvalue, eigenvector = np.linalg.eig(H_delta) vector_delta = eigenvector[:, np.argsort(np.real(eigenvalue))] vector_array.append(vector_delta) - Wilson_loop = 1 + wilson_loop = 1 dim = len(index_of_bands) for i0 in range(len(vector_array)-1): dot_matrix = np.zeros((dim , dim), dtype=complex) @@ -1636,7 +1636,7 @@ def calculate_chern_number_for_square_lattice_with_Wilson_loop_for_degenerate_ca i02 += 1 i01 += 1 det_value = np.linalg.det(dot_matrix) - Wilson_loop = Wilson_loop*det_value + wilson_loop = wilson_loop*det_value dot_matrix_plus = np.zeros((dim , dim), dtype=complex) i01 = 0 for dim1 in index_of_bands: @@ -1646,8 +1646,8 @@ def calculate_chern_number_for_square_lattice_with_Wilson_loop_for_degenerate_ca i02 += 1 i01 += 1 det_value = np.linalg.det(dot_matrix_plus) - Wilson_loop = Wilson_loop*det_value - arg = np.log(Wilson_loop)/1j + wilson_loop = wilson_loop*det_value + arg = np.log(wilson_loop)/1j chern_number = chern_number + arg chern_number = chern_number/(2*math.pi) return chern_number