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0.0.148

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guanjihuan 2022-10-21 05:16:23 +08:00
parent 8488629b94
commit d38cc1b8a8
4 changed files with 23 additions and 3 deletions
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2
API_Reference.py
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@ -216,6 +216,8 @@ conductance = guan.calculate_conductance_with_barrier(fermi_energy, h00, h01, le
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_slice_disorder(fermi_energy, h00, h01, disorder_intensity=2.0, disorder_concentration=1.0, 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)
conductance_array = guan.calculate_conductance_with_disorder_concentration_array(fermi_energy, h00, h01, disorder_concentration_array, disorder_intensity=2.0, 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 = 0.0.147
version = 0.0.148
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: 0.0.147
Version: 0.0.148
Summary: An open source python package
Home-page: https://py.guanjihuan.com
Author: guanjihuan

20
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-0.0.147, updated on December 17, 2022.
# The current version is guan-0.0.148, updated on December 21, 2022.
# Installation: pip install --upgrade guan
@ -1195,6 +1195,24 @@ def calculate_conductance_with_disorder(fermi_energy, h00, h01, disorder_intensi
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_slice_disorder(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]
for ix in range(length):
if np.random.uniform(0, 1)<=disorder_concentration:
disorder = np.random.uniform(-disorder_intensity, disorder_intensity)*np.eye(dim)
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_disorder_intensity_array(fermi_energy, h00, h01, disorder_intensity_array, disorder_concentration=1.0, length=100, calculation_times=1, print_show=0):
dim = np.array(disorder_intensity_array).shape[0]
conductance_array = np.zeros(dim)