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0.0.113

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guanjihuan 2022-07-20 16:21:40 +08:00
parent 9e81112614
commit 87ef3a433a
3 changed files with 23 additions and 2 deletions
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2
API_Reference.py
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@ -209,6 +209,8 @@ conductance = guan.calculate_conductance(fermi_energy, h00, h01, length=100)
conductance_array = guan.calculate_conductance_with_fermi_energy_array(fermi_energy_array, h00, h01, length=100, print_show=0)
conductance = guan.calculate_conductance_with_barrier(fermi_energy, h00, h01, length=100, barrier_length=20, barrier_potential=1)
conductance = guan.calculate_conductance_with_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)

2
PyPI/setup.cfg
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@ -1,7 +1,7 @@
[metadata]
# replace with your username:
name = guan
version = 0.0.112
version = 0.0.113
author = guanjihuan
author_email = guanjihuan@163.com
description = An open source python package

21
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-0.0.112, updated on July 20, 2022.
# The current version is guan-0.0.113, updated on July 20, 2022.
# Installation: pip install --upgrade guan
@ -1137,6 +1137,25 @@ def calculate_conductance_with_fermi_energy_array(fermi_energy_array, h00, h01,
i0 += 1
return conductance_array
def calculate_conductance_with_barrier(fermi_energy, h00, h01, length=100, barrier_length=20, barrier_potential=1):
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 ix == 0:
green_nn_n = guan.green_function(fermi_energy, h00, broadening=0, self_energy=left_self_energy)
green_0n_n = copy.deepcopy(green_nn_n)
elif int(length/2-barrier_length/2)<=ix<int(length/2+barrier_length/2):
green_nn_n = guan.green_function_nn_n(fermi_energy, h00+barrier_potential*np.identity(dim), h01, green_nn_n, broadening=0)
green_0n_n = guan.green_function_in_n(green_0n_n, h01, green_nn_n)
elif ix != length-1:
green_nn_n = guan.green_function_nn_n(fermi_energy, h00, 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, 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(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]