guan-0.0.29

This commit is contained in:
guanjihuan 2021-11-11 03:36:53 +08:00
parent 0d76353803
commit c5906e7e6d
6 changed files with 69 additions and 55 deletions

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@ -66,6 +66,11 @@ green_nn_n = guan.green_function_nn_n(fermi_energy, h00, h01, green_nn_n_minus,
green_in_n = guan.green_function_in_n(green_in_n_minus, h01, green_nn_n) green_in_n = guan.green_function_in_n(green_in_n_minus, h01, green_nn_n)
green_ni_n = guan.green_function_ni_n(green_nn_n, h01, green_ni_n_minus) green_ni_n = guan.green_function_ni_n(green_nn_n, h01, green_ni_n_minus)
green_ii_n = guan.green_function_ii_n(green_ii_n_minus, green_in_n_minus, h01, green_nn_n, green_ni_n_minus) green_ii_n = guan.green_function_ii_n(green_ii_n_minus, green_in_n_minus, h01, green_nn_n, green_ni_n_minus)
transfer = guan.transfer_matrix(fermi_energy, h00, h01)
right_lead_surface, left_lead_surface = guan.surface_green_function_of_lead(fermi_energy, h00, h01)
right_self_energy, left_self_energy = guan.self_energy_of_lead(fermi_energy, h00, h01)
right_self_energy, left_self_energy = self_energy_of_lead_with_h_LC_and_h_CR(fermi_energy, h00, h01, h_LC, h_CR)
green, gamma_right, gamma_left = green_function_with_leads(fermi_energy, h00, h01, h_LC, h_CR, center_hamiltonian)
# calculate density of states # Source code: https://py.guanjihuan.com/calculate_density_of_states # calculate density of states # Source code: https://py.guanjihuan.com/calculate_density_of_states
total_dos = guan.total_density_of_states(fermi_energy, hamiltonian, broadening=0.01) total_dos = guan.total_density_of_states(fermi_energy, hamiltonian, broadening=0.01)
@ -77,9 +82,6 @@ local_dos = guan.local_density_of_states_for_cubic_lattice_using_dyson_equation(
local_dos = guan.local_density_of_states_for_square_lattice_with_self_energy_using_dyson_equation(fermi_energy, h00, h01, N2, N1, right_self_energy, left_self_energy, internal_degree=1, broadening=0.01) local_dos = guan.local_density_of_states_for_square_lattice_with_self_energy_using_dyson_equation(fermi_energy, h00, h01, N2, N1, right_self_energy, left_self_energy, internal_degree=1, broadening=0.01)
# calculate conductance # Source code: https://py.guanjihuan.com/calculate_conductance # calculate conductance # Source code: https://py.guanjihuan.com/calculate_conductance
transfer = guan.transfer_matrix(fermi_energy, h00, h01)
right_lead_surface, left_lead_surface = guan.surface_green_function_of_lead(fermi_energy, h00, h01)
right_self_energy, left_self_energy = guan.self_energy_of_lead(fermi_energy, h00, h01)
conductance = guan.calculate_conductance(fermi_energy, h00, h01, length=100) conductance = guan.calculate_conductance(fermi_energy, h00, h01, length=100)
conductance_array = guan.calculate_conductance_with_fermi_energy_array(fermi_energy_array, h00, h01, length=100) conductance_array = guan.calculate_conductance_with_fermi_energy_array(fermi_energy_array, h00, h01, length=100)
conductance = guan.calculate_conductance_with_disorder(fermi_energy, h00, h01, disorder_intensity=2.0, disorder_concentration=1.0, length=100) conductance = guan.calculate_conductance_with_disorder(fermi_energy, h00, h01, disorder_intensity=2.0, disorder_concentration=1.0, length=100)

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@ -1,7 +1,7 @@
[metadata] [metadata]
# replace with your username: # replace with your username:
name = guan name = guan
version = 0.0.28 version = 0.0.29
author = guanjihuan author = guanjihuan
author_email = guanjihuan@163.com author_email = guanjihuan@163.com
description = An open source python package description = An open source python package

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@ -33,3 +33,62 @@ def green_function_ni_n(green_nn_n, h01, green_ni_n_minus):
def green_function_ii_n(green_ii_n_minus, green_in_n_minus, h01, green_nn_n, green_ni_n_minus): 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) 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 return green_ii_n
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 self_energy_of_lead_with_h_LC_and_h_CR(fermi_energy, h00, h01, h_LC, h_CR):
h_LC = np.array(h_LC)
h_CR = np.array(h_CR)
right_lead_surface, left_lead_surface = surface_green_function_of_lead(fermi_energy, h00, h01)
right_self_energy = np.dot(np.dot(h_CR, right_lead_surface), h_CR.transpose().conj())
left_self_energy = np.dot(np.dot(h_LC.transpose().conj(), left_lead_surface), h_LC)
return right_self_energy, left_self_energy
def green_function_with_leads(fermi_energy, h00, h01, h_LC, h_CR, center_hamiltonian):
dim = np.array(center_hamiltonian).shape[0]
right_self_energy, left_self_energy = self_energy_of_lead_with_h_LC_and_h_CR(fermi_energy, h00, h01, h_LC, h_CR)
green = np.linalg.inv(fermi_energy*np.identity(dim)-center_hamiltonian-left_self_energy-right_self_energy)
gamma_right = (right_self_energy - right_self_energy.transpose().conj())*1j
gamma_left = (left_self_energy - left_self_energy.transpose().conj())*1j
return green, gamma_right, gamma_left

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@ -11,8 +11,7 @@ def calculate_eigenvalue(hamiltonian):
if np.array(hamiltonian).shape==(): if np.array(hamiltonian).shape==():
eigenvalue = np.real(hamiltonian) eigenvalue = np.real(hamiltonian)
else: else:
eigenvalue, eigenvector = np.linalg.eig(hamiltonian) eigenvalue, eigenvector = np.linalg.eigh(hamiltonian)
eigenvalue = np.sort(np.real(eigenvalue))
return eigenvalue return eigenvalue
def calculate_eigenvalue_with_one_parameter(x_array, hamiltonian_function): def calculate_eigenvalue_with_one_parameter(x_array, hamiltonian_function):
@ -64,8 +63,7 @@ def calculate_eigenvalue_with_two_parameters(x_array, y_array, hamiltonian_funct
## calculate wave functions ## calculate wave functions
def calculate_eigenvector(hamiltonian): def calculate_eigenvector(hamiltonian):
eigenvalue, eigenvector = np.linalg.eig(hamiltonian) eigenvalue, eigenvector = np.linalg.eigh(hamiltonian)
eigenvector = eigenvector[:, np.argsort(np.real(eigenvalue))]
return eigenvector return eigenvector
## find vector with the same gauge ## find vector with the same gauge

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@ -6,49 +6,6 @@ import numpy as np
import copy import copy
from .calculate_Green_functions import * from .calculate_Green_functions import *
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): def calculate_conductance(fermi_energy, h00, h01, length=100):
right_self_energy, left_self_energy = self_energy_of_lead(fermi_energy, h00, h01) right_self_energy, left_self_energy = self_energy_of_lead(fermi_energy, h00, h01)
for ix in range(length): for ix in range(length):

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@ -5,8 +5,6 @@
import numpy as np import numpy as np
import copy import copy
from .calculate_Green_functions import * from .calculate_Green_functions import *
from .calculate_conductance import *
def if_active_channel(k_of_channel): def if_active_channel(k_of_channel):
if np.abs(np.imag(k_of_channel))<1e-6: if np.abs(np.imag(k_of_channel))<1e-6: