From 29f1310b39316d30ca698c40f839d0ee1c09dbf7 Mon Sep 17 00:00:00 2001 From: guanjihuan <34735497+guanjihuan@users.noreply.github.com> Date: Thu, 24 Jun 2021 09:42:35 +0800 Subject: [PATCH] update --- PyPI/build/lib/guan/__init__.py | 985 -------------------- PyPI/dist/guan-0.0.1-py3-none-any.whl | Bin 9044 -> 0 bytes PyPI/dist/guan-0.0.1.tar.gz | Bin 9564 -> 0 bytes PyPI/src/guan.egg-info/PKG-INFO | 19 - PyPI/src/guan.egg-info/SOURCES.txt | 9 - PyPI/src/guan.egg-info/dependency_links.txt | 1 - PyPI/src/guan.egg-info/top_level.txt | 1 - 7 files changed, 1015 deletions(-) delete mode 100644 PyPI/build/lib/guan/__init__.py delete mode 100644 PyPI/dist/guan-0.0.1-py3-none-any.whl delete mode 100644 PyPI/dist/guan-0.0.1.tar.gz delete mode 100644 PyPI/src/guan.egg-info/PKG-INFO delete mode 100644 PyPI/src/guan.egg-info/SOURCES.txt delete mode 100644 PyPI/src/guan.egg-info/dependency_links.txt delete mode 100644 PyPI/src/guan.egg-info/top_level.txt diff --git a/PyPI/build/lib/guan/__init__.py b/PyPI/build/lib/guan/__init__.py deleted file mode 100644 index 1db4c8b..0000000 --- a/PyPI/build/lib/guan/__init__.py +++ /dev/null @@ -1,985 +0,0 @@ -import numpy as np -import cmath -from math import * -import copy - - - - -# test - -def test(): - print('\nSuccess in the installation of GUAN package!\n') - - - - -# basic functions - -## Pauli matrices - -def sigma_0(): - return np.eye(2) - -def sigma_x(): - return np.array([[0, 1],[1, 0]]) - -def sigma_y(): - return np.array([[0, -1j],[1j, 0]]) - -def sigma_z(): - return np.array([[1, 0],[0, -1]]) - - -## Kronecker product of Pauli matrices - -def sigma_00(): - return np.kron(sigma_0(), sigma_0()) - -def sigma_0x(): - return np.kron(sigma_0(), sigma_x()) - -def sigma_0y(): - return np.kron(sigma_0(), sigma_y()) - -def sigma_0z(): - return np.kron(sigma_0(), sigma_z()) - - -def sigma_x0(): - return np.kron(sigma_x(), sigma_0()) - -def sigma_xx(): - return np.kron(sigma_x(), sigma_x()) - -def sigma_xy(): - return np.kron(sigma_x(), sigma_y()) - -def sigma_xz(): - return np.kron(sigma_x(), sigma_z()) - - -def sigma_y0(): - return np.kron(sigma_y(), sigma_0()) - -def sigma_yx(): - return np.kron(sigma_y(), sigma_x()) - -def sigma_yy(): - return np.kron(sigma_y(), sigma_y()) - -def sigma_yz(): - return np.kron(sigma_y(), sigma_z()) - - -def sigma_z0(): - return np.kron(sigma_z(), sigma_0()) - -def sigma_zx(): - return np.kron(sigma_z(), sigma_x()) - -def sigma_zy(): - return np.kron(sigma_z(), sigma_y()) - -def sigma_zz(): - return np.kron(sigma_z(), sigma_z()) - - - - -# Hermitian Hamiltonian of tight binding model - -def finite_size_along_one_direction(N, on_site=0, hopping=1, period=0): - on_site = np.array(on_site) - hopping = np.array(hopping) - if on_site.shape==(): - dim = 1 - else: - dim = on_site.shape[0] - hamiltonian = np.zeros((N*dim, N*dim), dtype=complex) - for i0 in range(N): - hamiltonian[i0*dim+0:i0*dim+dim, i0*dim+0:i0*dim+dim] = on_site - for i0 in range(N-1): - hamiltonian[i0*dim+0:i0*dim+dim, (i0+1)*dim+0:(i0+1)*dim+dim] = hopping - hamiltonian[(i0+1)*dim+0:(i0+1)*dim+dim, i0*dim+0:i0*dim+dim] = hopping.transpose().conj() - if period == 1: - hamiltonian[(N-1)*dim+0:(N-1)*dim+dim, 0:dim] = hopping - hamiltonian[0:dim, (N-1)*dim+0:(N-1)*dim+dim] = hopping.transpose().conj() - return hamiltonian - - -def finite_size_along_two_directions_for_square_lattice(N1, N2, on_site=0, hopping_1=1, hopping_2=1, period_1=0, period_2=0): - on_site = np.array(on_site) - hopping_1 = np.array(hopping_1) - hopping_2 = np.array(hopping_2) - if on_site.shape==(): - dim = 1 - else: - dim = on_site.shape[0] - hamiltonian = np.zeros((N1*N2*dim, N1*N2*dim), dtype=complex) - for i1 in range(N1): - for i2 in range(N2): - hamiltonian[i1*N2*dim+i2*dim+0:i1*N2*dim+i2*dim+dim, i1*N2*dim+i2*dim+0:i1*N2*dim+i2*dim+dim] = on_site - for i1 in range(N1-1): - for i2 in range(N2): - hamiltonian[i1*N2*dim+i2*dim+0:i1*N2*dim+i2*dim+dim, (i1+1)*N2*dim+i2*dim+0:(i1+1)*N2*dim+i2*dim+dim] = hopping_1 - hamiltonian[(i1+1)*N2*dim+i2*dim+0:(i1+1)*N2*dim+i2*dim+dim, i1*N2*dim+i2*dim+0:i1*N2*dim+i2*dim+dim] = hopping_1.transpose().conj() - for i1 in range(N1): - for i2 in range(N2-1): - hamiltonian[i1*N2*dim+i2*dim+0:i1*N2*dim+i2*dim+dim, i1*N2*dim+(i2+1)*dim+0:i1*N2*dim+(i2+1)*dim+dim] = hopping_2 - hamiltonian[i1*N2*dim+(i2+1)*dim+0:i1*N2*dim+(i2+1)*dim+dim, i1*N2*dim+i2*dim+0:i1*N2*dim+i2*dim+dim] = hopping_2.transpose().conj() - if period_1 == 1: - for i2 in range(N2): - hamiltonian[(N1-1)*N2*dim+i2*dim+0:(N1-1)*N2*dim+i2*dim+dim, i2*dim+0:i2*dim+dim] = hopping_1 - hamiltonian[i2*dim+0:i2*dim+dim, (N1-1)*N2*dim+i2*dim+0:(N1-1)*N2*dim+i2*dim+dim] = hopping_1.transpose().conj() - if period_2 == 1: - for i1 in range(N1): - hamiltonian[i1*N2*dim+(N2-1)*dim+0:i1*N2*dim+(N2-1)*dim+dim, i1*N2*dim+0:i1*N2*dim+dim] = hopping_2 - hamiltonian[i1*N2*dim+0:i1*N2*dim+dim, i1*N2*dim+(N2-1)*dim+0:i1*N2*dim+(N2-1)*dim+dim] = hopping_2.transpose().conj() - return hamiltonian - - -def finite_size_along_three_directions_for_cubic_lattice(N1, N2, N3, on_site=0, hopping_1=1, hopping_2=1, hopping_3=1, period_1=0, period_2=0, period_3=0): - on_site = np.array(on_site) - hopping_1 = np.array(hopping_1) - hopping_2 = np.array(hopping_2) - hopping_3 = np.array(hopping_3) - if on_site.shape==(): - dim = 1 - else: - dim = on_site.shape[0] - hamiltonian = np.zeros((N1*N2*N3*dim, N1*N2*N3*dim), dtype=complex) - for i1 in range(N1): - for i2 in range(N2): - for i3 in range(N3): - hamiltonian[i1*N2*N3*dim+i2*N3*dim+i3*dim+0:i1*N2*N3*dim+i2*N3*dim+i3*dim+dim, i1*N2*N3*dim+i2*N3*dim+i3*dim+0:i1*N2*N3*dim+i2*N3*dim+i3*dim+dim] = on_site - for i1 in range(N1-1): - for i2 in range(N2): - for i3 in range(N3): - hamiltonian[i1*N2*N3*dim+i2*N3*dim+i3*dim+0:i1*N2*N3*dim+i2*N3*dim+i3*dim+dim, (i1+1)*N2*N3*dim+i2*N3*dim+i3*dim+0:(i1+1)*N2*N3*dim+i2*N3*dim+i3*dim+dim] = hopping_1 - hamiltonian[(i1+1)*N2*N3*dim+i2*N3*dim+i3*dim+0:(i1+1)*N2*N3*dim+i2*N3*dim+i3*dim+dim, i1*N2*N3*dim+i2*N3*dim+i3*dim+0:i1*N2*N3*dim+i2*N3*dim+i3*dim+dim] = hopping_1.transpose().conj() - for i1 in range(N1): - for i2 in range(N2-1): - for i3 in range(N3): - hamiltonian[i1*N2*N3*dim+i2*N3*dim+i3*dim+0:i1*N2*N3*dim+i2*N3*dim+i3*dim+dim, i1*N2*N3*dim+(i2+1)*N3*dim+i3*dim+0:i1*N2*N3*dim+(i2+1)*N3*dim+i3*dim+dim] = hopping_2 - hamiltonian[i1*N2*N3*dim+(i2+1)*N3*dim+i3*dim+0:i1*N2*N3*dim+(i2+1)*N3*dim+i3*dim+dim, i1*N2*N3*dim+i2*N3*dim+i3*dim+0:i1*N2*N3*dim+i2*N3*dim+i3*dim+dim] = hopping_2.transpose().conj() - for i1 in range(N1): - for i2 in range(N2): - for i3 in range(N3-1): - hamiltonian[i1*N2*N3*dim+i2*N3*dim+i3*dim+0:i1*N2*N3*dim+i2*N3*dim+i3*dim+dim, i1*N2*N3*dim+i2*N3*dim+(i3+1)*dim+0:i1*N2*N3*dim+i2*N3*dim+(i3+1)*dim+dim] = hopping_3 - hamiltonian[i1*N2*N3*dim+i2*N3*dim+(i3+1)*dim+0:i1*N2*N3*dim+i2*N3*dim+(i3+1)*dim+dim, i1*N2*N3*dim+i2*N3*dim+i3*dim+0:i1*N2*N3*dim+i2*N3*dim+i3*dim+dim] = hopping_3.transpose().conj() - if period_1 == 1: - for i2 in range(N2): - for i3 in range(N3): - hamiltonian[(N1-1)*N2*N3*dim+i2*N3*dim+i3*dim+0:(N1-1)*N2*N3*dim+i2*N3*dim+i3*dim+dim, i2*N3*dim+i3*dim+0:i2*N3*dim+i3*dim+dim] = hopping_1 - hamiltonian[i2*N3*dim+i3*dim+0:i2*N3*dim+i3*dim+dim, (N1-1)*N2*N3*dim+i2*N3*dim+i3*dim+0:(N1-1)*N2*N3*dim+i2*N3*dim+i3*dim+dim] = hopping_1.transpose().conj() - if period_2 == 1: - for i1 in range(N1): - for i3 in range(N3): - hamiltonian[i1*N2*N3*dim+(N2-1)*N3*dim+i3*dim+0:i1*N2*N3*dim+(N2-1)*N3*dim+i3*dim+dim, i1*N2*N3*dim+i3*dim+0:i1*N2*N3*dim+i3*dim+dim] = hopping_2 - hamiltonian[i1*N2*N3*dim+i3*dim+0:i1*N2*N3*dim+i3*dim+dim, i1*N2*N3*dim+(N2-1)*N3*dim+i3*dim+0:i1*N2*N3*dim+(N2-1)*N3*dim+i3*dim+dim] = hopping_2.transpose().conj() - if period_3 == 1: - for i1 in range(N1): - for i2 in range(N2): - hamiltonian[i1*N2*N3*dim+i2*N3*dim+(N3-1)*dim+0:i1*N2*N3*dim+i2*N3*dim+(N3-1)*dim+dim, i1*N2*N3*dim+i2*N3*dim+0:i1*N2*N3*dim+i2*N3*dim+dim] = hopping_3 - hamiltonian[i1*N2*N3*dim+i2*N3*dim+0:i1*N2*N3*dim+i2*N3*dim+dim, i1*N2*N3*dim+i2*N3*dim+(N3-1)*dim+0:i1*N2*N3*dim+i2*N3*dim+(N3-1)*dim+dim] = hopping_3.transpose().conj() - return hamiltonian - - -def one_dimensional_fourier_transform(k, unit_cell, hopping): - unit_cell = np.array(unit_cell) - hopping = np.array(hopping) - hamiltonian = unit_cell+hopping*cmath.exp(1j*k)+hopping.transpose().conj()*cmath.exp(-1j*k) - return hamiltonian - - -def two_dimensional_fourier_transform_for_square_lattice(k1, k2, unit_cell, hopping_1, hopping_2): - unit_cell = np.array(unit_cell) - hopping_1 = np.array(hopping_1) - hopping_2 = np.array(hopping_2) - hamiltonian = unit_cell+hopping_1*cmath.exp(1j*k1)+hopping_1.transpose().conj()*cmath.exp(-1j*k1)+hopping_2*cmath.exp(1j*k2)+hopping_2.transpose().conj()*cmath.exp(-1j*k2) - return hamiltonian - - -def three_dimensional_fourier_transform_for_cubic_lattice(k1, k2, k3, unit_cell, hopping_1, hopping_2, hopping_3): - unit_cell = np.array(unit_cell) - hopping_1 = np.array(hopping_1) - hopping_2 = np.array(hopping_2) - hopping_3 = np.array(hopping_3) - hamiltonian = unit_cell+hopping_1*cmath.exp(1j*k1)+hopping_1.transpose().conj()*cmath.exp(-1j*k1)+hopping_2*cmath.exp(1j*k2)+hopping_2.transpose().conj()*cmath.exp(-1j*k2)+hopping_3*cmath.exp(1j*k3)+hopping_3.transpose().conj()*cmath.exp(-1j*k3) - return hamiltonian - - - - -# Hamiltonian of graphene lattice - -def hopping_along_zigzag_direction_for_graphene(N): - hopping = np.zeros((4*N, 4*N), dtype=complex) - for i0 in range(N): - hopping[4*i0+1, 4*i0+0] = 1 - hopping[4*i0+2, 4*i0+3] = 1 - return hopping - - -def finite_size_along_two_directions_for_graphene(N1, N2, period_1=0, period_2=0): - on_site = finite_size_along_one_direction(4) - hopping_1 = hopping_along_zigzag_direction_for_graphene(1) - hopping_2 = np.zeros((4, 4), dtype=complex) - hopping_2[3, 0] = 1 - hamiltonian = finite_size_along_two_directions_for_square_lattice(N1, N2, on_site, hopping_1, hopping_2, period_1, period_2) - return hamiltonian - - - - -# calculate band structures - -def calculate_eigenvalue(hamiltonian): - if np.array(hamiltonian).shape==(): - eigenvalue = np.real(hamiltonian) - else: - eigenvalue, eigenvector = np.linalg.eig(hamiltonian) - eigenvalue = np.sort(np.real(eigenvalue)) - return eigenvalue - - -def calculate_eigenvalue_with_one_parameter(x, hamiltonian_function): - dim_x = np.array(x).shape[0] - i0 = 0 - if np.array(hamiltonian_function(0)).shape==(): - eigenvalue_array = np.zeros((dim_x, 1)) - for x0 in x: - hamiltonian = hamiltonian_function(x0) - eigenvalue_array[i0, 0] = np.real(hamiltonian) - i0 += 1 - else: - dim = np.array(hamiltonian_function(0)).shape[0] - eigenvalue_array = np.zeros((dim_x, dim)) - for x0 in x: - hamiltonian = hamiltonian_function(x0) - eigenvalue, eigenvector = np.linalg.eig(hamiltonian) - eigenvalue_array[i0, :] = np.sort(np.real(eigenvalue[:])) - i0 += 1 - return eigenvalue_array - - -def calculate_eigenvalue_with_two_parameters(x, y, hamiltonian_function): - dim_x = np.array(x).shape[0] - dim_y = np.array(y).shape[0] - if np.array(hamiltonian_function(0,0)).shape==(): - eigenvalue_array = np.zeros((dim_y, dim_x, 1)) - i0 = 0 - for y0 in y: - j0 = 0 - for x0 in x: - hamiltonian = hamiltonian_function(x0, y0) - eigenvalue_array[i0, j0, 0] = np.real(hamiltonian) - j0 += 1 - i0 += 1 - else: - dim = np.array(hamiltonian_function(0, 0)).shape[0] - eigenvalue_array = np.zeros((dim_y, dim_x, dim)) - i0 = 0 - for y0 in y: - j0 = 0 - for x0 in x: - hamiltonian = hamiltonian_function(x0, y0) - eigenvalue, eigenvector = np.linalg.eig(hamiltonian) - eigenvalue_array[i0, j0, :] = np.sort(np.real(eigenvalue[:])) - j0 += 1 - i0 += 1 - return eigenvalue_array - - - - -# calculate wave functions - -def calculate_eigenvector(hamiltonian): - eigenvalue, eigenvector = np.linalg.eig(hamiltonian) - eigenvector = eigenvector[:, np.argsort(np.real(eigenvalue))] - return eigenvector - - - - -# calculate Green functions - -def green_function(fermi_energy, hamiltonian, broadening, self_energy=0): - if np.array(hamiltonian).shape==(): - dim = 1 - else: - dim = np.array(hamiltonian).shape[0] - green = np.linalg.inv((fermi_energy+broadening*1j)*np.eye(dim)-hamiltonian-self_energy) - return green - - -def green_function_nn_n(fermi_energy, h00, h01, green_nn_n_minus, broadening, self_energy=0): - h01 = np.array(h01) - if np.array(h00).shape==(): - dim = 1 - else: - dim = np.array(h00).shape[0] - green_nn_n = np.linalg.inv((fermi_energy+broadening*1j)*np.identity(dim)-h00-np.dot(np.dot(h01.transpose().conj(), green_nn_n_minus), h01)-self_energy) - return green_nn_n - - -def green_function_in_n(green_in_n_minus, h01, green_nn_n): - green_in_n = np.dot(np.dot(green_in_n_minus, h01), green_nn_n) - return green_in_n - - -def green_function_ni_n(green_nn_n, h01, green_ni_n_minus): - h01 = np.array(h01) - green_ni_n = np.dot(np.dot(green_nn_n, h01.transpose().conj()), green_ni_n_minus) - return green_ni_n - - -def green_function_ii_n(green_ii_n_minus, green_in_n_minus, h01, green_nn_n, green_ni_n_minus): - green_ii_n = green_ii_n_minus+np.dot(np.dot(np.dot(np.dot(green_in_n_minus, h01), green_nn_n), h01.transpose().conj()),green_ni_n_minus) - return green_ii_n - - - - -# calculate density of states - -def total_density_of_states(fermi_energy, hamiltonian, broadening=0.01): - green = green_function(fermi_energy, hamiltonian, broadening) - total_dos = -np.trace(np.imag(green))/pi - return total_dos - - -def total_density_of_states_with_fermi_energy_array(fermi_energy_array, hamiltonian, broadening=0.01): - dim = np.array(fermi_energy_array).shape[0] - total_dos_array = np.zeros(dim) - i0 = 0 - for fermi_energy in fermi_energy_array: - total_dos_array[i0] = total_density_of_states(fermi_energy, hamiltonian, broadening) - i0 += 1 - return total_dos_array - - -def local_density_of_states_for_square_lattice(fermi_energy, hamiltonian, N1, N2, internal_degree=1, broadening=0.01): - # dim_hamiltonian = N1*N2*internal_degree - green = green_function(fermi_energy, hamiltonian, broadening) - local_dos = np.zeros((N2, N1)) - for i1 in range(N1): - for i2 in range(N2): - for i in range(internal_degree): - local_dos[i2, i1] = local_dos[i2, i1]-np.imag(green[i1*N2*internal_degree+i2*internal_degree+i, i1*N2*internal_degree+i2*internal_degree+i])/pi - return local_dos - - -def local_density_of_states_for_cubic_lattice(fermi_energy, hamiltonian, N1, N2, N3, internal_degree=1, broadening=0.01): - # dim_hamiltonian = N1*N2*N3*internal_degree - green = green_function(fermi_energy, hamiltonian, broadening) - local_dos = np.zeros((N3, N2, N1)) - for i1 in range(N1): - for i2 in range(N2): - for i3 in range(N3): - for i in range(internal_degree): - local_dos[i3, i2, i1] = local_dos[i3, i2, i1]-np.imag(green[i1*N2*N3*internal_degree+i2*N3*internal_degree+i3*internal_degree+i, i1*N2*N3*internal_degree+i2*N3*internal_degree+i3*internal_degree+i])/pi - return local_dos - - -def local_density_of_states_for_square_lattice_using_dyson_equation(fermi_energy, h00, h01, N2, N1, internal_degree=1, broadening=0.01): - # dim_h00 = N2*internal_degree - local_dos = np.zeros((N2, N1)) - green_11_1 = green_function(fermi_energy, h00, broadening) - for i1 in range(N1): - green_nn_n_minus = green_11_1 - green_in_n_minus = green_11_1 - green_ni_n_minus = green_11_1 - green_ii_n_minus = green_11_1 - for i2_0 in range(i1): - green_nn_n = green_function_nn_n(fermi_energy, h00, h01, green_nn_n_minus, broadening) - green_nn_n_minus = green_nn_n - if i1!=0: - green_in_n_minus = green_nn_n - green_ni_n_minus = green_nn_n - green_ii_n_minus = green_nn_n - for size_0 in range(N1-1-i1): - green_nn_n = green_function_nn_n(fermi_energy, h00, h01, green_nn_n_minus, broadening) - green_nn_n_minus = green_nn_n - green_ii_n = green_function_ii_n(green_ii_n_minus, green_in_n_minus, h01, green_nn_n, green_ni_n_minus) - green_ii_n_minus = green_ii_n - green_in_n = green_function_in_n(green_in_n_minus, h01, green_nn_n) - green_in_n_minus = green_in_n - green_ni_n = green_function_ni_n(green_nn_n, h01, green_ni_n_minus) - green_ni_n_minus = green_ni_n - for i2 in range(N2): - for i in range(internal_degree): - local_dos[i2, i1] = local_dos[i2, i1] - np.imag(green_ii_n_minus[i2*internal_degree+i, i2*internal_degree+i])/pi - return local_dos - - -def local_density_of_states_for_cubic_lattice_using_dyson_equation(fermi_energy, h00, h01, N3, N2, N1, internal_degree=1, broadening=0.01): - # dim_h00 = N2*N3*internal_degree - local_dos = np.zeros((N3, N2, N1)) - green_11_1 = green_function(fermi_energy, h00, broadening) - for i1 in range(N1): - green_nn_n_minus = green_11_1 - green_in_n_minus = green_11_1 - green_ni_n_minus = green_11_1 - green_ii_n_minus = green_11_1 - for i1_0 in range(i1): - green_nn_n = green_function_nn_n(fermi_energy, h00, h01, green_nn_n_minus, broadening) - green_nn_n_minus = green_nn_n - if i1!=0: - green_in_n_minus = green_nn_n - green_ni_n_minus = green_nn_n - green_ii_n_minus = green_nn_n - for size_0 in range(N1-1-i1): - green_nn_n = green_function_nn_n(fermi_energy, h00, h01, green_nn_n_minus, broadening) - green_nn_n_minus = green_nn_n - green_ii_n = green_function_ii_n(green_ii_n_minus, green_in_n_minus, h01, green_nn_n, green_ni_n_minus) - green_ii_n_minus = green_ii_n - green_in_n = green_function_in_n(green_in_n_minus, h01, green_nn_n) - green_in_n_minus = green_in_n - green_ni_n = green_function_ni_n(green_nn_n, h01, green_ni_n_minus) - green_ni_n_minus = green_ni_n - for i2 in range(N2): - for i3 in range(N3): - for i in range(internal_degree): - local_dos[i3, i2, i1] = local_dos[i3, i2, i1] -np.imag(green_ii_n_minus[i2*N3*internal_degree+i3*internal_degree+i, i2*N3*internal_degree+i3*internal_degree+i])/pi - return local_dos - - - - -# calculate conductance - -def transfer_matrix(fermi_energy, h00, h01): - h01 = np.array(h01) - if np.array(h00).shape==(): - dim = 1 - else: - dim = np.array(h00).shape[0] - transfer = np.zeros((2*dim, 2*dim), dtype=complex) - transfer[0:dim, 0:dim] = np.dot(np.linalg.inv(h01), fermi_energy*np.identity(dim)-h00) - transfer[0:dim, dim:2*dim] = np.dot(-1*np.linalg.inv(h01), h01.transpose().conj()) - transfer[dim:2*dim, 0:dim] = np.identity(dim) - transfer[dim:2*dim, dim:2*dim] = 0 - return transfer - - -def surface_green_function_of_lead(fermi_energy, h00, h01): - h01 = np.array(h01) - if np.array(h00).shape==(): - dim = 1 - else: - dim = np.array(h00).shape[0] - fermi_energy = fermi_energy+1e-9*1j - transfer = transfer_matrix(fermi_energy, h00, h01) - eigenvalue, eigenvector = np.linalg.eig(transfer) - ind = np.argsort(np.abs(eigenvalue)) - temp = np.zeros((2*dim, 2*dim), dtype=complex) - i0 = 0 - for ind0 in ind: - temp[:, i0] = eigenvector[:, ind0] - i0 += 1 - s1 = temp[dim:2*dim, 0:dim] - s2 = temp[0:dim, 0:dim] - s3 = temp[dim:2*dim, dim:2*dim] - s4 = temp[0:dim, dim:2*dim] - right_lead_surface = np.linalg.inv(fermi_energy*np.identity(dim)-h00-np.dot(np.dot(h01, s2), np.linalg.inv(s1))) - left_lead_surface = np.linalg.inv(fermi_energy*np.identity(dim)-h00-np.dot(np.dot(h01.transpose().conj(), s3), np.linalg.inv(s4))) - return right_lead_surface, left_lead_surface - - -def self_energy_of_lead(fermi_energy, h00, h01): - h01 = np.array(h01) - right_lead_surface, left_lead_surface = surface_green_function_of_lead(fermi_energy, h00, h01) - right_self_energy = np.dot(np.dot(h01, right_lead_surface), h01.transpose().conj()) - left_self_energy = np.dot(np.dot(h01.transpose().conj(), left_lead_surface), h01) - return right_self_energy, left_self_energy - - -def calculate_conductance(fermi_energy, h00, h01, length=100): - right_self_energy, left_self_energy = self_energy_of_lead(fermi_energy, h00, h01) - for ix in range(length): - if ix == 0: - green_nn_n = green_function(fermi_energy, h00, broadening=0, self_energy=left_self_energy) - green_0n_n = copy.deepcopy(green_nn_n) - elif ix != length-1: - green_nn_n = green_function_nn_n(fermi_energy, h00, h01, green_nn_n, broadening=0) - green_0n_n = green_function_in_n(green_0n_n, h01, green_nn_n) - else: - green_nn_n = green_function_nn_n(fermi_energy, h00, h01, green_nn_n, broadening=0, self_energy=right_self_energy) - green_0n_n = green_function_in_n(green_0n_n, h01, green_nn_n) - right_self_energy = (right_self_energy - right_self_energy.transpose().conj())*(0+1j) - left_self_energy = (left_self_energy - left_self_energy.transpose().conj())*(0+1j) - conductance = np.trace(np.dot(np.dot(np.dot(left_self_energy, green_0n_n), right_self_energy), green_0n_n.transpose().conj())) - return conductance - - -def calculate_conductance_with_fermi_energy_array(fermi_energy_array, h00, h01, length=100): - dim = np.array(fermi_energy_array).shape[0] - conductance_array = np.zeros(dim) - i0 = 0 - for fermi_energy_0 in fermi_energy_array: - conductance_array[i0] = np.real(calculate_conductance(fermi_energy_0, h00, h01, length)) - i0 += 1 - return conductance_array - - - - -# calculate scattering matrix - -def if_active_channel(k_of_channel): - if np.abs(np.imag(k_of_channel))<1e-6: - if_active = 1 - else: - if_active = 0 - return if_active - - -def get_k_and_velocity_of_channel(fermi_energy, h00, h01): - if np.array(h00).shape==(): - dim = 1 - else: - dim = np.array(h00).shape[0] - transfer = transfer_matrix(fermi_energy, h00, h01) - eigenvalue, eigenvector = np.linalg.eig(transfer) - k_of_channel = np.log(eigenvalue)/1j - ind = np.argsort(np.real(k_of_channel)) - k_of_channel = np.sort(k_of_channel) - temp = np.zeros((2*dim, 2*dim), dtype=complex) - temp2 = np.zeros((2*dim), dtype=complex) - i0 = 0 - for ind0 in ind: - temp[:, i0] = eigenvector[:, ind0] - temp2[i0] = eigenvalue[ind0] - i0 += 1 - eigenvalue = copy.deepcopy(temp2) - temp = temp[0:dim, :] - factor = np.zeros(2*dim, dtype=complex) - for dim0 in range(dim): - factor = factor+np.square(np.abs(temp[dim0, :])) - for dim0 in range(2*dim): - temp[:, dim0] = temp[:, dim0]/np.sqrt(factor[dim0]) - velocity_of_channel = np.zeros((2*dim), dtype=complex) - for dim0 in range(2*dim): - velocity_of_channel[dim0] = eigenvalue[dim0]*np.dot(np.dot(temp[0:dim, :].transpose().conj(), h01),temp[0:dim, :])[dim0, dim0] - velocity_of_channel = -2*np.imag(velocity_of_channel) - eigenvector = copy.deepcopy(temp) - return k_of_channel, velocity_of_channel, eigenvalue, eigenvector - - -def get_classified_k_velocity_u_and_f(fermi_energy, h00, h01): - if np.array(h00).shape==(): - dim = 1 - else: - dim = np.array(h00).shape[0] - k_of_channel, velocity_of_channel, eigenvalue, eigenvector = get_k_and_velocity_of_channel(fermi_energy, h00, h01) - ind_right_active = 0; ind_right_evanescent = 0; ind_left_active = 0; ind_left_evanescent = 0 - k_right = np.zeros(dim, dtype=complex); k_left = np.zeros(dim, dtype=complex) - velocity_right = np.zeros(dim, dtype=complex); velocity_left = np.zeros(dim, dtype=complex) - lambda_right = np.zeros(dim, dtype=complex); lambda_left = np.zeros(dim, dtype=complex) - u_right = np.zeros((dim, dim), dtype=complex); u_left = np.zeros((dim, dim), dtype=complex) - for dim0 in range(2*dim): - if_active = if_active_channel(k_of_channel[dim0]) - if if_active_channel(k_of_channel[dim0]) == 1: - direction = np.sign(velocity_of_channel[dim0]) - else: - direction = np.sign(np.imag(k_of_channel[dim0])) - if direction == 1: - if if_active == 1: # right-moving active channel - k_right[ind_right_active] = k_of_channel[dim0] - velocity_right[ind_right_active] = velocity_of_channel[dim0] - lambda_right[ind_right_active] = eigenvalue[dim0] - u_right[:, ind_right_active] = eigenvector[:, dim0] - ind_right_active += 1 - else: # right-moving evanescent channel - k_right[dim-1-ind_right_evanescent] = k_of_channel[dim0] - velocity_right[dim-1-ind_right_evanescent] = velocity_of_channel[dim0] - lambda_right[dim-1-ind_right_evanescent] = eigenvalue[dim0] - u_right[:, dim-1-ind_right_evanescent] = eigenvector[:, dim0] - ind_right_evanescent += 1 - else: - if if_active == 1: # left-moving active channel - k_left[ind_left_active] = k_of_channel[dim0] - velocity_left[ind_left_active] = velocity_of_channel[dim0] - lambda_left[ind_left_active] = eigenvalue[dim0] - u_left[:, ind_left_active] = eigenvector[:, dim0] - ind_left_active += 1 - else: # left-moving evanescent channel - k_left[dim-1-ind_left_evanescent] = k_of_channel[dim0] - velocity_left[dim-1-ind_left_evanescent] = velocity_of_channel[dim0] - lambda_left[dim-1-ind_left_evanescent] = eigenvalue[dim0] - u_left[:, dim-1-ind_left_evanescent] = eigenvector[:, dim0] - ind_left_evanescent += 1 - lambda_matrix_right = np.diag(lambda_right) - lambda_matrix_left = np.diag(lambda_left) - f_right = np.dot(np.dot(u_right, lambda_matrix_right), np.linalg.inv(u_right)) - f_left = np.dot(np.dot(u_left, lambda_matrix_left), np.linalg.inv(u_left)) - return k_right, k_left, velocity_right, velocity_left, f_right, f_left, u_right, u_left, ind_right_active - - -def calculate_scattering_matrix(fermi_energy, h00, h01, length=100): - h01 = np.array(h01) - if np.array(h00).shape==(): - dim = 1 - else: - dim = np.array(h00).shape[0] - k_right, k_left, velocity_right, velocity_left, f_right, f_left, u_right, u_left, ind_right_active = get_classified_k_velocity_u_and_f(fermi_energy, h00, h01) - right_self_energy = np.dot(h01, f_right) - left_self_energy = np.dot(h01.transpose().conj(), np.linalg.inv(f_left)) - for i0 in range(length): - if i0 == 0: - green_nn_n = green_function(fermi_energy, h00, broadening=0, self_energy=left_self_energy) - green_00_n = copy.deepcopy(green_nn_n) - green_0n_n = copy.deepcopy(green_nn_n) - green_n0_n = copy.deepcopy(green_nn_n) - elif i0 != length-1: - green_nn_n = green_function_nn_n(fermi_energy, h00, h01, green_nn_n, broadening=0) - else: - green_nn_n = green_function_nn_n(fermi_energy, h00, h01, green_nn_n, broadening=0, self_energy=right_self_energy) - green_00_n = green_function_ii_n(green_00_n, green_0n_n, h01, green_nn_n, green_n0_n) - green_0n_n = green_function_in_n(green_0n_n, h01, green_nn_n) - green_n0_n = green_function_ni_n(green_nn_n, h01, green_n0_n) - temp = np.dot(h01.transpose().conj(), np.linalg.inv(f_right)-np.linalg.inv(f_left)) - transmission_matrix = np.dot(np.dot(np.linalg.inv(u_right), np.dot(green_n0_n, temp)), u_right) - reflection_matrix = np.dot(np.dot(np.linalg.inv(u_left), np.dot(green_00_n, temp)-np.identity(dim)), u_right) - for dim0 in range(dim): - for dim1 in range(dim): - if_active = if_active_channel(k_right[dim0])*if_active_channel(k_right[dim1]) - if if_active == 1: - transmission_matrix[dim0, dim1] = np.sqrt(np.abs(velocity_right[dim0]/velocity_right[dim1])) * transmission_matrix[dim0, dim1] - reflection_matrix[dim0, dim1] = np.sqrt(np.abs(velocity_left[dim0]/velocity_right[dim1]))*reflection_matrix[dim0, dim1] - else: - transmission_matrix[dim0, dim1] = 0 - reflection_matrix[dim0, dim1] = 0 - sum_of_tran_refl_array = np.sum(np.square(np.abs(transmission_matrix[0:ind_right_active, 0:ind_right_active])), axis=0)+np.sum(np.square(np.abs(reflection_matrix[0:ind_right_active, 0:ind_right_active])), axis=0) - for sum_of_tran_refl in sum_of_tran_refl_array: - if sum_of_tran_refl > 1.001: - print('Error Alert: scattering matrix is not normalized!') - return transmission_matrix, reflection_matrix, k_right, k_left, velocity_right, velocity_left, ind_right_active - - -def print_or_write_scattering_matrix(fermi_energy, h00, h01, length=100, on_print=1, on_write=0): - if np.array(h00).shape==(): - dim = 1 - else: - dim = np.array(h00).shape[0] - transmission_matrix, reflection_matrix, k_right, k_left, velocity_right, velocity_left, ind_right_active = calculate_scattering_matrix(fermi_energy, h00, h01, length) - if on_print == 1: - print('\nActive channel (left or right) = ', ind_right_active) - print('Evanescent channel (left or right) = ', dim-ind_right_active, '\n') - print('K of right-moving active channels:\n', np.real(k_right[0:ind_right_active])) - print('K of left-moving active channels:\n', np.real(k_left[0:ind_right_active]), '\n') - print('Velocity of right-moving active channels:\n', np.real(velocity_right[0:ind_right_active])) - print('Velocity of left-moving active channels:\n', np.real(velocity_left[0:ind_right_active]), '\n') - print('Transmission matrix:\n', np.square(np.abs(transmission_matrix[0:ind_right_active, 0:ind_right_active]))) - print('Reflection matrix:\n', np.square(np.abs(reflection_matrix[0:ind_right_active, 0:ind_right_active])), '\n') - print('Total transmission of channels:\n', np.sum(np.square(np.abs(transmission_matrix[0:ind_right_active, 0:ind_right_active])), axis=0)) - print('Total reflection of channels:\n',np.sum(np.square(np.abs(reflection_matrix[0:ind_right_active, 0:ind_right_active])), axis=0)) - print('Sum of transmission and reflection of channels:\n', np.sum(np.square(np.abs(transmission_matrix[0:ind_right_active, 0:ind_right_active])), axis=0) + np.sum(np.square(np.abs(reflection_matrix[0:ind_right_active, 0:ind_right_active])), axis=0)) - print('Total conductance = ', np.sum(np.square(np.abs(transmission_matrix[0:ind_right_active, 0:ind_right_active]))), '\n') - if on_write == 1: - with open('a.txt', 'w') as f: - f.write('Active channel (left or right) = ' + str(ind_right_active) + '\n') - f.write('Evanescent channel (left or right) = ' + str(dim - ind_right_active) + '\n\n') - f.write('Channel K Velocity\n') - for ind0 in range(ind_right_active): - f.write(' '+str(ind0 + 1) + ' | '+str(np.real(k_right[ind0]))+' ' + str(np.real(velocity_right[ind0]))+'\n') - f.write('\n') - for ind0 in range(ind_right_active): - f.write(' -' + str(ind0 + 1) + ' | ' + str(np.real(k_left[ind0])) + ' ' + str(np.real(velocity_left[ind0])) + '\n') - f.write('\nScattering matrix:\n ') - for ind0 in range(ind_right_active): - f.write(str(ind0+1)+' ') - f.write('\n') - for ind1 in range(ind_right_active): - f.write(' '+str(ind1+1)+' ') - for ind2 in range(ind_right_active): - f.write('%f' % np.square(np.abs(transmission_matrix[ind1, ind2]))+' ') - f.write('\n') - f.write('\n') - for ind1 in range(ind_right_active): - f.write(' -'+str(ind1+1)+' ') - for ind2 in range(ind_right_active): - f.write('%f' % np.square(np.abs(reflection_matrix[ind1, ind2]))+' ') - f.write('\n') - f.write('\n') - f.write('Total transmission of channels:\n'+str(np.sum(np.square(np.abs(transmission_matrix[0:ind_right_active, 0:ind_right_active])), axis=0))+'\n') - f.write('Total conductance = '+str(np.sum(np.square(np.abs(transmission_matrix[0:ind_right_active, 0:ind_right_active]))))+'\n') - - - - -# calculate Chern number - -def calculate_chern_number_for_square_lattice(hamiltonian_function, precision=100): - if np.array(hamiltonian_function(0, 0)).shape==(): - dim = 1 - else: - dim = np.array(hamiltonian_function(0, 0)).shape[0] - delta = 2*pi/precision - chern_number = np.zeros(dim, dtype=complex) - for kx in np.arange(-pi, pi, delta): - for ky in np.arange(-pi, pi, delta): - H = hamiltonian_function(kx, ky) - vector = calculate_eigenvector(H) - H_delta_kx = hamiltonian_function(kx+delta, ky) - vector_delta_kx = calculate_eigenvector(H_delta_kx) - H_delta_ky = hamiltonian_function(kx, ky+delta) - vector_delta_ky = calculate_eigenvector(H_delta_ky) - H_delta_kx_ky = hamiltonian_function(kx+delta, ky+delta) - vector_delta_kx_ky = calculate_eigenvector(H_delta_kx_ky) - for i in range(dim): - vector_i = vector[:, i] - vector_delta_kx_i = vector_delta_kx[:, i] - vector_delta_ky_i = vector_delta_ky[:, i] - vector_delta_kx_ky_i = vector_delta_kx_ky[:, i] - Ux = np.dot(np.conj(vector_i), vector_delta_kx_i)/abs(np.dot(np.conj(vector_i), vector_delta_kx_i)) - Uy = np.dot(np.conj(vector_i), vector_delta_ky_i)/abs(np.dot(np.conj(vector_i), vector_delta_ky_i)) - Ux_y = np.dot(np.conj(vector_delta_ky_i), vector_delta_kx_ky_i)/abs(np.dot(np.conj(vector_delta_ky_i), vector_delta_kx_ky_i)) - Uy_x = np.dot(np.conj(vector_delta_kx_i), vector_delta_kx_ky_i)/abs(np.dot(np.conj(vector_delta_kx_i), vector_delta_kx_ky_i)) - F = cmath.log(Ux*Uy_x*(1/Ux_y)*(1/Uy)) - chern_number[i] = chern_number[i] + F - chern_number = chern_number/(2*pi*1j) - return chern_number - - - - -# calculate Wilson loop - -def calculate_wilson_loop(hamiltonian_function, k_min=-pi, k_max=pi, precision=100): - k_array = np.linspace(k_min, k_max, precision) - dim = np.array(hamiltonian_function(0)).shape[0] - wilson_loop_array = np.ones(dim, dtype=complex) - for i in range(dim): - eigenvector_array = [] - for k in k_array: - eigenvector = calculate_eigenvector(hamiltonian_function(k)) - if k != k_max: - eigenvector_array.append(eigenvector[:, i]) - else: - eigenvector_array.append(eigenvector_array[0]) - for i0 in range(precision-1): - F = np.dot(eigenvector_array[i0+1].transpose().conj(), eigenvector_array[i0]) - wilson_loop_array[i] = np.dot(F, wilson_loop_array[i]) - return wilson_loop_array - - - - -# read and write - -def read_one_dimensional_data(filename='a'): - f = open(filename+'.txt', 'r') - text = f.read() - f.close() - row_list = np.array(text.split('\n')) - dim_column = np.array(row_list[0].split()).shape[0] - x = np.array([]) - y = np.array([]) - for row in row_list: - column = np.array(row.split()) - if column.shape[0] != 0: - x = np.append(x, [float(column[0])], axis=0) - y_row = np.zeros(dim_column-1) - for dim0 in range(dim_column-1): - y_row[dim0] = float(column[dim0+1]) - if np.array(y).shape[0] == 0: - y = [y_row] - else: - y = np.append(y, [y_row], axis=0) - return x, y - - -def read_two_dimensional_data(filename='a'): - f = open(filename+'.txt', 'r') - text = f.read() - f.close() - row_list = np.array(text.split('\n')) - dim_column = np.array(row_list[0].split()).shape[0] - x = np.array([]) - y = np.array([]) - matrix = np.array([]) - for i0 in range(row_list.shape[0]): - column = np.array(row_list[i0].split()) - if i0 == 0: - x_str = column[1::] - x = np.zeros(x_str.shape[0]) - for i00 in range(x_str.shape[0]): - x[i00] = float(x_str[i00]) - elif column.shape[0] != 0: - y = np.append(y, [float(column[0])], axis=0) - matrix_row = np.zeros(dim_column-1) - for dim0 in range(dim_column-1): - matrix_row[dim0] = float(column[dim0+1]) - if np.array(matrix).shape[0] == 0: - matrix = [matrix_row] - else: - matrix = np.append(matrix, [matrix_row], axis=0) - return x, y, matrix - - -def write_one_dimensional_data(x, y, filename='a'): - with open(filename+'.txt', 'w') as f: - i0 = 0 - for x0 in x: - f.write(str(x0)+' ') - if len(y.shape) == 1: - f.write(str(y[i0])+'\n') - elif len(y.shape) == 2: - for j0 in range(y.shape[1]): - f.write(str(y[i0, j0])+' ') - f.write('\n') - i0 += 1 - - -def write_two_dimensional_data(x, y, matrix, filename='a'): - with open(filename+'.txt', 'w') as f: - f.write('0 ') - for x0 in x: - f.write(str(x0)+' ') - f.write('\n') - i0 = 0 - for y0 in y: - f.write(str(y0)) - j0 = 0 - for x0 in x: - f.write(' '+str(matrix[i0, j0])+' ') - j0 += 1 - f.write('\n') - i0 += 1 - - - - -# plot figures - -def plot(x, y, xlabel='x', ylabel='y', title='', filename='a', show=1, save=0, type='', y_min=None, y_max=None): - import matplotlib.pyplot as plt - fig, ax = plt.subplots() - plt.subplots_adjust(bottom=0.20, left=0.18) - ax.plot(x, y, type) - ax.grid() - ax.set_title(title, fontsize=20, fontfamily='Times New Roman') - ax.set_xlabel(xlabel, fontsize=20, fontfamily='Times New Roman') - ax.set_ylabel(ylabel, fontsize=20, fontfamily='Times New Roman') - if y_min!=None or y_max!=None: - if y_min==None: - y_min=min(y) - if y_max==None: - y_max=max(y) - ax.set_ylim(y_min, y_max) - ax.tick_params(labelsize=20) - labels = ax.get_xticklabels() + ax.get_yticklabels() - [label.set_fontname('Times New Roman') for label in labels] - if save == 1: - plt.savefig(filename+'.jpg', dpi=300) - if show == 1: - plt.show() - plt.close('all') - - -def plot_3d_surface(x, y, matrix, xlabel='x', ylabel='y', zlabel='z', title='', filename='a', show=1, save=0, z_min=None, z_max=None): - import matplotlib.pyplot as plt - from matplotlib import cm - from matplotlib.ticker import LinearLocator - matrix = np.array(matrix) - fig, ax = plt.subplots(subplot_kw={"projection": "3d"}) - plt.subplots_adjust(bottom=0.1, right=0.65) - x, y = np.meshgrid(x, y) - if len(matrix.shape) == 2: - surf = ax.plot_surface(x, y, matrix, cmap=cm.coolwarm, linewidth=0, antialiased=False) - elif len(matrix.shape) == 3: - for i0 in range(matrix.shape[2]): - surf = ax.plot_surface(x, y, matrix[:,:,i0], cmap=cm.coolwarm, linewidth=0, antialiased=False) - ax.set_title(title, fontsize=20, fontfamily='Times New Roman') - ax.set_xlabel(xlabel, fontsize=20, fontfamily='Times New Roman') - ax.set_ylabel(ylabel, fontsize=20, fontfamily='Times New Roman') - ax.set_zlabel(zlabel, fontsize=20, fontfamily='Times New Roman') - ax.zaxis.set_major_locator(LinearLocator(5)) - ax.zaxis.set_major_formatter('{x:.2f}') - if z_min!=None or z_max!=None: - if z_min==None: - z_min=matrix.min() - if z_max==None: - z_max=matrix.max() - ax.set_zlim(z_min, z_max) - ax.tick_params(labelsize=15) - labels = ax.get_xticklabels() + ax.get_yticklabels() + ax.get_zticklabels() - [label.set_fontname('Times New Roman') for label in labels] - cax = plt.axes([0.80, 0.15, 0.05, 0.75]) - cbar = fig.colorbar(surf, cax=cax) - cbar.ax.tick_params(labelsize=15) - for l in cbar.ax.yaxis.get_ticklabels(): - l.set_family('Times New Roman') - if save == 1: - plt.savefig(filename+'.jpg', dpi=300) - if show == 1: - plt.show() - plt.close('all') - - -def plot_contour(x, y, matrix, xlabel='x', ylabel='y', title='', filename='a', show=1, save=0): - import matplotlib.pyplot as plt - fig, ax = plt.subplots() - plt.subplots_adjust(bottom=0.2, right=0.75, left = 0.16) - x, y = np.meshgrid(x, y) - contour = ax.contourf(x,y,matrix,cmap='jet') - ax.set_title(title, fontsize=20, fontfamily='Times New Roman') - ax.set_xlabel(xlabel, fontsize=20, fontfamily='Times New Roman') - ax.set_ylabel(ylabel, fontsize=20, fontfamily='Times New Roman') - ax.tick_params(labelsize=15) - labels = ax.get_xticklabels() + ax.get_yticklabels() - [label.set_fontname('Times New Roman') for label in labels] - cax = plt.axes([0.78, 0.17, 0.08, 0.71]) - cbar = fig.colorbar(contour, cax=cax) - cbar.ax.tick_params(labelsize=15) - for l in cbar.ax.yaxis.get_ticklabels(): - l.set_family('Times New Roman') - if save == 1: - plt.savefig(filename+'.jpg', dpi=300) - if show == 1: - plt.show() - plt.close('all') - - - - -# download - -def download_with_scihub(address=None, num=1): - from bs4 import BeautifulSoup - import re - import requests - import os - if num==1 and address!=None: - address_array = [address] - else: - address_array = [] - for i in range(num): - address = input('\nInput：') - address_array.append(address) - for address in address_array: - r = requests.post('https://sci-hub.st/', data={'request': address}) - print('\nResponse：', r) - print('Address：', r.url) - soup = BeautifulSoup(r.text, features='lxml') - pdf_URL = soup.iframe['src'] - if re.search(re.compile('^https:'), pdf_URL): - pass - else: - pdf_URL = 'https:'+pdf_URL - print('PDF address：', pdf_URL) - name = re.search(re.compile('fdp.*?/'),pdf_URL[::-1]).group()[::-1][1::] - print('PDF name：', name) - print('Directory：', os.getcwd()) - print('\nDownloading...') - r = requests.get(pdf_URL, stream=True) - with open(name, 'wb') as f: - for chunk in r.iter_content(chunk_size=32): - f.write(chunk) - print('Completed!\n') - if num != 1: - print('All completed!\n') \ No newline at end of file diff --git a/PyPI/dist/guan-0.0.1-py3-none-any.whl b/PyPI/dist/guan-0.0.1-py3-none-any.whl deleted file 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Programming Language :: Python :: 3 -Classifier: License :: OSI Approved :: MIT License -Classifier: Operating System :: OS Independent -Requires-Python: >=3.6 -Description-Content-Type: text/markdown -License-File: LICENSE - -GUAN is an open-source python package developed and maintained by https://www.guanjihuan.com. The primary location of this package is on website https://py.guanjihuan.com. - diff --git a/PyPI/src/guan.egg-info/SOURCES.txt b/PyPI/src/guan.egg-info/SOURCES.txt deleted file mode 100644 index d26d474..0000000 --- a/PyPI/src/guan.egg-info/SOURCES.txt +++ /dev/null @@ -1,9 +0,0 @@ -LICENSE -README.md -pyproject.toml -setup.cfg -src/guan/__init__.py -src/guan.egg-info/PKG-INFO -src/guan.egg-info/SOURCES.txt -src/guan.egg-info/dependency_links.txt -src/guan.egg-info/top_level.txt \ No newline at end of file diff --git a/PyPI/src/guan.egg-info/dependency_links.txt b/PyPI/src/guan.egg-info/dependency_links.txt deleted file mode 100644 index 8b13789..0000000 --- a/PyPI/src/guan.egg-info/dependency_links.txt +++ /dev/null @@ -1 +0,0 @@ - diff --git a/PyPI/src/guan.egg-info/top_level.txt b/PyPI/src/guan.egg-info/top_level.txt deleted file mode 100644 index ff25243..0000000 --- a/PyPI/src/guan.egg-info/top_level.txt +++ /dev/null @@ -1 +0,0 @@ -guan