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0.0.149

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guanjihuan 2022-10-21 05:34:14 +08:00
parent d38cc1b8a8
commit d75ca3db2f
3 changed files with 4 additions and 3 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 = 0.0.148 version = 0.0.149
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: 0.0.148 Version: 0.0.149
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

3
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-0.0.148, updated on December 21, 2022. # The current version is guan-0.0.149, updated on December 21, 2022.
# Installation: pip install --upgrade guan # Installation: pip install --upgrade guan
@ -1199,6 +1199,7 @@ def calculate_conductance_with_slice_disorder(fermi_energy, h00, h01, disorder_i
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):
disorder = np.zeros((dim, dim))
if np.random.uniform(0, 1)<=disorder_concentration: if np.random.uniform(0, 1)<=disorder_concentration:
disorder = np.random.uniform(-disorder_intensity, disorder_intensity)*np.eye(dim) disorder = np.random.uniform(-disorder_intensity, disorder_intensity)*np.eye(dim)
if ix == 0: if ix == 0: