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0.0.169

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guanjihuan 2023-06-11 00:42:35 +08:00
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commit c2ef79d4a7
3 changed files with 11 additions and 5 deletions
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2
PyPI/setup.cfg
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@ -1,7 +1,7 @@
[metadata] [metadata]
# replace with your username: # replace with your username:
name = guan name = guan
version = 0.0.168 version = 0.0.169
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

2
PyPI/src/guan.egg-info/PKG-INFO
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@ -1,6 +1,6 @@
Metadata-Version: 2.1 Metadata-Version: 2.1
Name: guan Name: guan
Version: 0.0.168 Version: 0.0.169
Summary: An open source python package Summary: An open source python package
Home-page: https://py.guanjihuan.com Home-page: https://py.guanjihuan.com
Author: guanjihuan Author: guanjihuan

12
PyPI/src/guan/__init__.py
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@ -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. # 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-0.0.168, updated on April 25, 2023. # The current version is guan-0.0.169, updated on June 11, 2023.
# Installation: pip install --upgrade guan # Installation: pip install --upgrade guan
@ -30,7 +30,6 @@
import numpy as np import numpy as np
import math import math
import cmath import cmath
import copy
import guan import guan
@ -1194,6 +1193,7 @@ def local_density_of_states_for_square_lattice_with_self_energy_using_dyson_equa
# 计算电导 # 计算电导
def calculate_conductance(fermi_energy, h00, h01, length=100): def calculate_conductance(fermi_energy, h00, h01, length=100):
import copy
right_self_energy, left_self_energy, gamma_right, gamma_left = guan.self_energy_of_lead(fermi_energy, h00, h01) right_self_energy, left_self_energy, gamma_right, gamma_left = guan.self_energy_of_lead(fermi_energy, h00, h01)
for ix in range(length): for ix in range(length):
if ix == 0: if ix == 0:
@ -1222,6 +1222,7 @@ def calculate_conductance_with_fermi_energy_array(fermi_energy_array, h00, h01,
# 计算在势垒散射下的电导 # 计算在势垒散射下的电导
def calculate_conductance_with_barrier(fermi_energy, h00, h01, length=100, barrier_length=20, barrier_potential=1): def calculate_conductance_with_barrier(fermi_energy, h00, h01, length=100, barrier_length=20, barrier_potential=1):
import copy
right_self_energy, left_self_energy, gamma_right, gamma_left = guan.self_energy_of_lead(fermi_energy, h00, h01) right_self_energy, left_self_energy, gamma_right, gamma_left = guan.self_energy_of_lead(fermi_energy, h00, h01)
dim = np.array(h00).shape[0] dim = np.array(h00).shape[0]
for ix in range(length): for ix in range(length):
@ -1242,6 +1243,7 @@ def calculate_conductance_with_barrier(fermi_energy, h00, h01, length=100, barri
# 计算在无序散射下的电导 # 计算在无序散射下的电导
def calculate_conductance_with_disorder(fermi_energy, h00, h01, disorder_intensity=2.0, disorder_concentration=1.0, length=100, calculation_times=1): def calculate_conductance_with_disorder(fermi_energy, h00, h01, disorder_intensity=2.0, disorder_concentration=1.0, length=100, calculation_times=1):
import copy
right_self_energy, left_self_energy, gamma_right, gamma_left = guan.self_energy_of_lead(fermi_energy, h00, h01) right_self_energy, left_self_energy, gamma_right, gamma_left = guan.self_energy_of_lead(fermi_energy, h00, h01)
dim = np.array(h00).shape[0] dim = np.array(h00).shape[0]
conductance_averaged = 0 conductance_averaged = 0
@ -1267,6 +1269,7 @@ def calculate_conductance_with_disorder(fermi_energy, h00, h01, disorder_intensi
# 计算在无序垂直切片的散射下的电导 # 计算在无序垂直切片的散射下的电导
def calculate_conductance_with_slice_disorder(fermi_energy, h00, h01, disorder_intensity=2.0, disorder_concentration=1.0, length=100): def calculate_conductance_with_slice_disorder(fermi_energy, h00, h01, disorder_intensity=2.0, disorder_concentration=1.0, length=100):
import copy
right_self_energy, left_self_energy, gamma_right, gamma_left = guan.self_energy_of_lead(fermi_energy, h00, h01) right_self_energy, left_self_energy, gamma_right, gamma_left = guan.self_energy_of_lead(fermi_energy, h00, h01)
dim = np.array(h00).shape[0] dim = np.array(h00).shape[0]
for ix in range(length+2): for ix in range(length+2):
@ -1287,6 +1290,7 @@ def calculate_conductance_with_slice_disorder(fermi_energy, h00, h01, disorder_i
# 计算在无序水平切片的散射下的电导 # 计算在无序水平切片的散射下的电导
def calculate_conductance_with_disorder_inside_unit_cell_which_keeps_translational_symmetry(fermi_energy, h00, h01, disorder_intensity=2.0, disorder_concentration=1.0, length=100): def calculate_conductance_with_disorder_inside_unit_cell_which_keeps_translational_symmetry(fermi_energy, h00, h01, disorder_intensity=2.0, disorder_concentration=1.0, length=100):
import copy
right_self_energy, left_self_energy, gamma_right, gamma_left = guan.self_energy_of_lead(fermi_energy, h00, h01) right_self_energy, left_self_energy, gamma_right, gamma_left = guan.self_energy_of_lead(fermi_energy, h00, h01)
dim = np.array(h00).shape[0] dim = np.array(h00).shape[0]
disorder = np.zeros((dim, dim)) disorder = np.zeros((dim, dim))
@ -1308,6 +1312,7 @@ def calculate_conductance_with_disorder_inside_unit_cell_which_keeps_translation
# 计算在随机空位的散射下的电导 # 计算在随机空位的散射下的电导
def calculate_conductance_with_random_vacancy(fermi_energy, h00, h01, vacancy_concentration=0.5, vacancy_potential=1e9, length=100): def calculate_conductance_with_random_vacancy(fermi_energy, h00, h01, vacancy_concentration=0.5, vacancy_potential=1e9, length=100):
import copy
right_self_energy, left_self_energy, gamma_right, gamma_left = guan.self_energy_of_lead(fermi_energy, h00, h01) right_self_energy, left_self_energy, gamma_right, gamma_left = guan.self_energy_of_lead(fermi_energy, h00, h01)
dim = np.array(h00).shape[0] dim = np.array(h00).shape[0]
for ix in range(length+2): for ix in range(length+2):
@ -1459,6 +1464,7 @@ def if_active_channel(k_of_channel):
# 获取通道的动量和速度，用于计算散射矩阵 # 获取通道的动量和速度，用于计算散射矩阵
def get_k_and_velocity_of_channel(fermi_energy, h00, h01): def get_k_and_velocity_of_channel(fermi_energy, h00, h01):
import copy
if np.array(h00).shape==(): if np.array(h00).shape==():
dim = 1 dim = 1
else: else:
@ -1541,6 +1547,7 @@ def get_classified_k_velocity_u_and_f(fermi_energy, h00, h01):
# 计算散射矩阵 # 计算散射矩阵
def calculate_scattering_matrix(fermi_energy, h00, h01, length=100): def calculate_scattering_matrix(fermi_energy, h00, h01, length=100):
import copy
h01 = np.array(h01) h01 = np.array(h01)
if np.array(h00).shape==(): if np.array(h00).shape==():
dim = 1 dim = 1
@ -1663,7 +1670,6 @@ def print_or_write_scattering_matrix(fermi_energy, h00, h01, length=100, print_s
# 在无序下，计算散射矩阵 # 在无序下，计算散射矩阵
def calculate_scattering_matrix_with_disorder(fermi_energy, h00, h01, length=100, disorder_intensity=2.0, disorder_concentration=1.0): def calculate_scattering_matrix_with_disorder(fermi_energy, h00, h01, length=100, disorder_intensity=2.0, disorder_concentration=1.0):
import copy import copy
import math
h01 = np.array(h01) h01 = np.array(h01)
if np.array(h00).shape==(): if np.array(h00).shape==():
dim = 1 dim = 1