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guanjihuan
2022-08-10 13:31:34 +08:00
parent 4ee73721f3
commit 7472379475
8 changed files with 34 additions and 7 deletions
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PyPI/setup.cfg
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[metadata] [metadata]
# replace with your username: # replace with your username:
name = guan name = guan
version = 0.0.117 version = 0.0.118
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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PyPI/src/guan.egg-info/PKG-INFO Normal file
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Metadata-Version: 2.1
Name: guan
Version: 0.0.118
Summary: An open source python package
Home-page: https://py.guanjihuan.com
Author: guanjihuan
Author-email: guanjihuan@163.com
Project-URL: Bug Tracker, https://py.guanjihuan.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Guan is an open-source python package developed and maintained by https://www.guanjihuan.com/about. The primary location of this package is on website https://py.guanjihuan.com. 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.

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PyPI/src/guan.egg-info/SOURCES.txt Normal file
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LICENSE
README.md
pyproject.toml
setup.cfg
src/guan/__init__.py
src/guan.egg-info/PKG-INFO
src/guan.egg-info/SOURCES.txt
src/guan.egg-info/dependency_links.txt
src/guan.egg-info/top_level.txt

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PyPI/src/guan.egg-info/dependency_links.txt Normal file
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PyPI/src/guan.egg-info/top_level.txt Normal file
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guan

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PyPI/src/guan/__init__.py
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# 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-0.0.117, updated on July 21, 2022. # The current version is guan-0.0.118, updated on August 10, 2022.
# Installation: pip install --upgrade guan # Installation: pip install --upgrade guan
@@ -1560,26 +1560,26 @@ def calculate_chern_number_for_square_lattice_with_Wilson_loop(hamiltonian_funct
for ky in np.arange(-math.pi, math.pi, delta): for ky in np.arange(-math.pi, math.pi, delta):
vector_array = [] vector_array = []
# line_1 # line_1
for i0 in range(precision_of_Wilson_loop+1): for i0 in range(precision_of_Wilson_loop):
H_delta = hamiltonian_function(kx+delta/precision_of_Wilson_loop*i0, ky) H_delta = hamiltonian_function(kx+delta/precision_of_Wilson_loop*i0, ky)
eigenvalue, eigenvector = np.linalg.eig(H_delta) eigenvalue, eigenvector = np.linalg.eig(H_delta)
vector_delta = eigenvector[:, np.argsort(np.real(eigenvalue))] vector_delta = eigenvector[:, np.argsort(np.real(eigenvalue))]
vector_array.append(vector_delta) vector_array.append(vector_delta)
# line_2 # line_2
for i0 in range(precision_of_Wilson_loop): for i0 in range(precision_of_Wilson_loop):
H_delta = hamiltonian_function(kx+delta, ky+delta/precision_of_Wilson_loop*(i0+1)) H_delta = hamiltonian_function(kx+delta, ky+delta/precision_of_Wilson_loop*i0)
eigenvalue, eigenvector = np.linalg.eig(H_delta) eigenvalue, eigenvector = np.linalg.eig(H_delta)
vector_delta = eigenvector[:, np.argsort(np.real(eigenvalue))] vector_delta = eigenvector[:, np.argsort(np.real(eigenvalue))]
vector_array.append(vector_delta) vector_array.append(vector_delta)
# line_3 # line_3
for i0 in range(precision_of_Wilson_loop): for i0 in range(precision_of_Wilson_loop):
H_delta = hamiltonian_function(kx+delta-delta/precision_of_Wilson_loop*(i0+1), ky+delta) H_delta = hamiltonian_function(kx+delta-delta/precision_of_Wilson_loop*i0, ky+delta)
eigenvalue, eigenvector = np.linalg.eig(H_delta) eigenvalue, eigenvector = np.linalg.eig(H_delta)
vector_delta = eigenvector[:, np.argsort(np.real(eigenvalue))] vector_delta = eigenvector[:, np.argsort(np.real(eigenvalue))]
vector_array.append(vector_delta) vector_array.append(vector_delta)
# line_4 # line_4
for i0 in range(precision_of_Wilson_loop-1): for i0 in range(precision_of_Wilson_loop):
H_delta = hamiltonian_function(kx, ky+delta-delta/precision_of_Wilson_loop*(i0+1)) H_delta = hamiltonian_function(kx, ky+delta-delta/precision_of_Wilson_loop*i0)
eigenvalue, eigenvector = np.linalg.eig(H_delta) eigenvalue, eigenvector = np.linalg.eig(H_delta)
vector_delta = eigenvector[:, np.argsort(np.real(eigenvalue))] vector_delta = eigenvector[:, np.argsort(np.real(eigenvalue))]
vector_array.append(vector_delta) vector_array.append(vector_delta)