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0.0.151

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guanjihuan
2022-10-25 15:56:00 +08:00
parent 5017d788a5
commit 92b7327119
4 changed files with 25 additions and 3 deletions
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2
API_Reference.py
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@@ -218,6 +218,8 @@ conductance = guan.calculate_conductance_with_disorder(fermi_energy, h00, h01, d
conductance = guan.calculate_conductance_with_slice_disorder(fermi_energy, h00, h01, disorder_intensity=2.0, disorder_concentration=1.0, length=100)
conductance = guan.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)
conductance = guan.calculate_conductance_with_random_vacancy(fermi_energy, h00, h01, vacancy_concentration=0.5, vacancy_potential=1e9, length=100)
conductance_array = guan.calculate_conductance_with_disorder_intensity_array(fermi_energy, h00, h01, disorder_intensity_array, disorder_concentration=1.0, length=100, calculation_times=1, print_show=0)

2
PyPI/setup.cfg
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@@ -1,7 +1,7 @@
[metadata]
# replace with your username:
name = guan
version = 0.0.150
version = 0.0.151
author = guanjihuan
author_email = guanjihuan@163.com
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
Name: guan
Version: 0.0.150
Version: 0.0.151
Summary: An open source python package
Home-page: https://py.guanjihuan.com
Author: guanjihuan

22
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.
# The current version is guan-0.0.150, updated on December 22, 2022.
# The current version is guan-0.0.151, updated on December 25, 2022.
# Installation: pip install --upgrade guan
@@ -1214,6 +1214,26 @@ def calculate_conductance_with_slice_disorder(fermi_energy, h00, h01, disorder_i
conductance = np.trace(np.dot(np.dot(np.dot(gamma_left, green_0n_n), gamma_right), green_0n_n.transpose().conj()))
return conductance
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):
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]
disorder = np.zeros((dim, dim))
for dim0 in range(dim):
if np.random.uniform(0, 1)<=disorder_concentration:
disorder[dim0, dim0] = np.random.uniform(-disorder_intensity, disorder_intensity)
for ix in range(length):
if ix == 0:
green_nn_n = guan.green_function(fermi_energy, h00+disorder, broadening=0, self_energy=left_self_energy)
green_0n_n = copy.deepcopy(green_nn_n)
elif ix != length-1:
green_nn_n = guan.green_function_nn_n(fermi_energy, h00+disorder, h01, green_nn_n, broadening=0)
green_0n_n = guan.green_function_in_n(green_0n_n, h01, green_nn_n)
else:
green_nn_n = guan.green_function_nn_n(fermi_energy, h00+disorder, h01, green_nn_n, broadening=0, self_energy=right_self_energy)
green_0n_n = guan.green_function_in_n(green_0n_n, h01, green_nn_n)
conductance = np.trace(np.dot(np.dot(np.dot(gamma_left, green_0n_n), gamma_right), green_0n_n.transpose().conj()))
return conductance
def calculate_conductance_with_random_vacancy(fermi_energy, h00, h01, vacancy_concentration=0.5, vacancy_potential=1e9, length=100):
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]