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0.0.154

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guanjihuan
2022-11-24 15:19:30 +08:00
parent 8658bd2053
commit 61c91480ab
3 changed files with 19 additions and 19 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 = 0.0.153 version = 0.0.154
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: 0.0.153 Version: 0.0.154
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

34
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-0.0.153, updated on November 17, 2022. # The current version is guan-0.0.154, updated on November 24, 2022.
# Installation: pip install --upgrade guan # Installation: pip install --upgrade guan
@@ -1178,16 +1178,16 @@ def calculate_conductance_with_barrier(fermi_energy, h00, h01, length=100, barri
def calculate_conductance_with_disorder(fermi_energy, h00, h01, disorder_intensity=2.0, disorder_concentration=1.0, length=100): 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) 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+2):
disorder = np.zeros((dim, dim)) disorder = np.zeros((dim, dim))
for dim0 in range(dim): for dim0 in range(dim):
if np.random.uniform(0, 1)<=disorder_concentration: if np.random.uniform(0, 1)<=disorder_concentration:
disorder[dim0, dim0] = np.random.uniform(-disorder_intensity, disorder_intensity) disorder[dim0, dim0] = np.random.uniform(-disorder_intensity, disorder_intensity)
if ix == 0: if ix == 0:
green_nn_n = guan.green_function(fermi_energy, h00+disorder, broadening=0, self_energy=left_self_energy) green_nn_n = guan.green_function(fermi_energy, h00, broadening=0, self_energy=left_self_energy)
green_0n_n = copy.deepcopy(green_nn_n) green_0n_n = copy.deepcopy(green_nn_n)
elif ix != length-1: elif ix != length+1:
green_nn_n = guan.green_function_nn_n(fermi_energy, h00+disorder, h01, green_nn_n, broadening=0) 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) green_0n_n = guan.green_function_in_n(green_0n_n, h01, green_nn_n)
else: 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_nn_n = guan.green_function_nn_n(fermi_energy, h00+disorder, h01, green_nn_n, broadening=0, self_energy=right_self_energy)
@@ -1198,15 +1198,15 @@ def calculate_conductance_with_disorder(fermi_energy, h00, h01, disorder_intensi
def calculate_conductance_with_slice_disorder(fermi_energy, h00, h01, disorder_intensity=2.0, disorder_concentration=1.0, length=100): 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) 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+2):
disorder = np.zeros((dim, dim)) 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:
green_nn_n = guan.green_function(fermi_energy, h00+disorder, broadening=0, self_energy=left_self_energy) green_nn_n = guan.green_function(fermi_energy, h00, broadening=0, self_energy=left_self_energy)
green_0n_n = copy.deepcopy(green_nn_n) green_0n_n = copy.deepcopy(green_nn_n)
elif ix != length-1: elif ix != length+1:
green_nn_n = guan.green_function_nn_n(fermi_energy, h00+disorder, h01, green_nn_n, broadening=0) 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) green_0n_n = guan.green_function_in_n(green_0n_n, h01, green_nn_n)
else: 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_nn_n = guan.green_function_nn_n(fermi_energy, h00+disorder, h01, green_nn_n, broadening=0, self_energy=right_self_energy)
@@ -1221,12 +1221,12 @@ def calculate_conductance_with_disorder_inside_unit_cell_which_keeps_translation
for dim0 in range(dim): for dim0 in range(dim):
if np.random.uniform(0, 1)<=disorder_concentration: if np.random.uniform(0, 1)<=disorder_concentration:
disorder[dim0, dim0] = np.random.uniform(-disorder_intensity, disorder_intensity) disorder[dim0, dim0] = np.random.uniform(-disorder_intensity, disorder_intensity)
for ix in range(length): for ix in range(length+2):
if ix == 0: if ix == 0:
green_nn_n = guan.green_function(fermi_energy, h00+disorder, broadening=0, self_energy=left_self_energy) green_nn_n = guan.green_function(fermi_energy, h00, broadening=0, self_energy=left_self_energy)
green_0n_n = copy.deepcopy(green_nn_n) green_0n_n = copy.deepcopy(green_nn_n)
elif ix != length-1: elif ix != length+1:
green_nn_n = guan.green_function_nn_n(fermi_energy, h00+disorder, h01, green_nn_n, broadening=0) 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) green_0n_n = guan.green_function_in_n(green_0n_n, h01, green_nn_n)
else: 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_nn_n = guan.green_function_nn_n(fermi_energy, h00+disorder, h01, green_nn_n, broadening=0, self_energy=right_self_energy)
@@ -1237,16 +1237,16 @@ def calculate_conductance_with_disorder_inside_unit_cell_which_keeps_translation
def calculate_conductance_with_random_vacancy(fermi_energy, h00, h01, vacancy_concentration=0.5, vacancy_potential=1e9, length=100): def calculate_conductance_with_random_vacancy(fermi_energy, h00, h01, vacancy_concentration=0.5, vacancy_potential=1e9, length=100):
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+2):
random_vacancy = np.zeros((dim, dim)) random_vacancy = np.zeros((dim, dim))
for dim0 in range(dim): for dim0 in range(dim):
if np.random.uniform(0, 1)<=vacancy_concentration: if np.random.uniform(0, 1)<=vacancy_concentration:
random_vacancy[dim0, dim0] = vacancy_potential random_vacancy[dim0, dim0] = vacancy_potential
if ix == 0: if ix == 0:
green_nn_n = guan.green_function(fermi_energy, h00+random_vacancy, broadening=0, self_energy=left_self_energy) green_nn_n = guan.green_function(fermi_energy, h00, broadening=0, self_energy=left_self_energy)
green_0n_n = copy.deepcopy(green_nn_n) green_0n_n = copy.deepcopy(green_nn_n)
elif ix != length-1: elif ix != length+1:
green_nn_n = guan.green_function_nn_n(fermi_energy, h00+random_vacancy, h01, green_nn_n, broadening=0) 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) green_0n_n = guan.green_function_in_n(green_0n_n, h01, green_nn_n)
else: else:
green_nn_n = guan.green_function_nn_n(fermi_energy, h00+random_vacancy, h01, green_nn_n, broadening=0, self_energy=right_self_energy) green_nn_n = guan.green_function_nn_n(fermi_energy, h00+random_vacancy, h01, green_nn_n, broadening=0, self_energy=right_self_energy)