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## Guan package
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## Guan package
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Guan is an open-source python package developed and maintained by https://www.guanjihuan.com/about (Ji-Huan Guan, 关济寰). 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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Guan is an open-source python package developed and maintained by https://www.guanjihuan.com/about (Ji-Huan Guan, 关济寰). 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 figure plotting, file-reading/writing, data processing, file processing.
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The primary location of this package is on website https://py.guanjihuan.com.
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The primary location of this package is on website https://py.guanjihuan.com.
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@ -23,11 +23,10 @@ import guan
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+ density of states
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+ density of states
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+ quantum transport
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+ quantum transport
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+ topological invariant
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+ topological invariant
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+ read and write
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+ plot figures
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+ plot figures
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+ read and write
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+ data processing
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+ data processing
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+ others
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+ file processing
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## About this package
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## About this package
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@ -1,7 +1,7 @@
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[metadata]
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[metadata]
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# replace with your username:
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# replace with your username:
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name = guan
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name = guan
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version = 0.0.179
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version = 0.0.180
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author = guanjihuan
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author = guanjihuan
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author_email = guanjihuan@163.com
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author_email = guanjihuan@163.com
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description = An open source python package
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description = An open source python package
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Metadata-Version: 2.1
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Metadata-Version: 2.1
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Name: guan
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Name: guan
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Version: 0.0.179
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Version: 0.0.180
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Summary: An open source python package
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Summary: An open source python package
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Home-page: https://py.guanjihuan.com
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Home-page: https://py.guanjihuan.com
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Author: guanjihuan
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Author: guanjihuan
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# Module 1: basic functions
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# Module 1: basic functions
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## test
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# 测试
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def test():
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def test():
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print('\nSuccess in the installation of Guan package!\n')
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print('\nSuccess in the installation of Guan package!\n')
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## Pauli matrices
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# 泡利矩阵
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def sigma_0():
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def sigma_0():
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import numpy as np
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import numpy as np
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return np.eye(2)
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return np.eye(2)
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@ -80,8 +78,7 @@ def sigma_z():
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import numpy as np
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import numpy as np
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return np.array([[1, 0],[0, -1]])
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return np.array([[1, 0],[0, -1]])
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## Kronecker product of Pauli matrices
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# 泡利矩阵的张量积
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def sigma_00():
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def sigma_00():
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import numpy as np
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import numpy as np
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import guan
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import guan
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@ -1568,8 +1565,6 @@ def local_density_of_states_for_square_lattice_with_self_energy_using_dyson_equa
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# Module 8: quantum transport
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# Module 8: quantum transport
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## conductance
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# 计算电导
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# 计算电导
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def calculate_conductance(fermi_energy, h00, h01, length=100):
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def calculate_conductance(fermi_energy, h00, h01, length=100):
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import numpy as np
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import numpy as np
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@ -1773,8 +1768,6 @@ def calculate_conductance_with_scattering_length_array(fermi_energy, h00, h01, l
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conductance_array = conductance_array/calculation_times
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conductance_array = conductance_array/calculation_times
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return conductance_array
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return conductance_array
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## multi-terminal transmission
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# 计算得到Gamma矩阵和格林函数,用于计算六端口的量子输运
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# 计算得到Gamma矩阵和格林函数,用于计算六端口的量子输运
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def get_gamma_array_and_green_for_six_terminal_transmission(fermi_energy, h00_for_lead_4, h01_for_lead_4, h00_for_lead_2, h01_for_lead_2, center_hamiltonian, width=10, length=50, internal_degree=1, moving_step_of_leads=10):
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def get_gamma_array_and_green_for_six_terminal_transmission(fermi_energy, h00_for_lead_4, h01_for_lead_4, h00_for_lead_2, h01_for_lead_2, center_hamiltonian, width=10, length=50, internal_degree=1, moving_step_of_leads=10):
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import numpy as np
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import numpy as np
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@ -1857,8 +1850,6 @@ def calculate_six_terminal_transmissions_from_lead_1(fermi_energy, h00_for_lead_
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transmission_16 = np.real(np.trace(np.dot(np.dot(np.dot(gamma_array[0], green), gamma_array[5]), green.transpose().conj())))
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transmission_16 = np.real(np.trace(np.dot(np.dot(np.dot(gamma_array[0], green), gamma_array[5]), green.transpose().conj())))
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return transmission_12, transmission_13, transmission_14, transmission_15, transmission_16
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return transmission_12, transmission_13, transmission_14, transmission_15, transmission_16
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## scattering matrix
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# 通过动量k的虚部,判断通道为传播通道还是衰减通道
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# 通过动量k的虚部,判断通道为传播通道还是衰减通道
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def if_active_channel(k_of_channel):
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def if_active_channel(k_of_channel):
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import numpy as np
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import numpy as np
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@ -3841,7 +3832,7 @@ def play_element_words(random_on=0, show_translation=1, show_link=1, translation
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# # Module 13: file processing
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# Module 13: file processing
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# 如果不存在文件夹,则新建文件夹
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# 如果不存在文件夹,则新建文件夹
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def make_directory(directory='./test'):
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def make_directory(directory='./test'):
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