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0.0.156

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guanjihuan
2022-11-29 18:09:31 +08:00
parent 2f8fdae7db
commit fc4249dd74
4 changed files with 49 additions and 20 deletions
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2
PyPI/setup.cfg
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@@ -1,7 +1,7 @@
[metadata]
# replace with your username:
name = guan
version = 0.0.155
version = 0.0.156
author = guanjihuan
author_email = guanjihuan@163.com
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
Name: guan
Version: 0.0.155
Version: 0.0.156
Summary: An open source python package
Home-page: https://py.guanjihuan.com
Author: guanjihuan

61
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-0.0.155, updated on November 24, 2022.
# The current version is guan-0.0.156, updated on November 29, 2022.
# Installation: pip install --upgrade guan
@@ -1175,24 +1175,28 @@ def calculate_conductance_with_barrier(fermi_energy, h00, h01, length=100, barri
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):
def calculate_conductance_with_disorder(fermi_energy, h00, h01, disorder_intensity=2.0, disorder_concentration=1.0, length=100, calculation_times=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+2):
disorder = np.zeros((dim, dim))
for dim0 in range(dim):
if np.random.uniform(0, 1)<=disorder_concentration:
disorder[dim0, dim0] = np.random.uniform(-disorder_intensity, disorder_intensity)
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 ix != length+1:
green_nn_n = guan.green_function_nn_n(fermi_energy, h00+disorder, 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()))
conductance_averaged = 0
for times in range(calculation_times):
for ix in range(length+2):
disorder = np.zeros((dim, dim))
for dim0 in range(dim):
if np.random.uniform(0, 1)<=disorder_concentration:
disorder[dim0, dim0] = np.random.uniform(-disorder_intensity, disorder_intensity)
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 ix != length+1:
green_nn_n = guan.green_function_nn_n(fermi_energy, h00+disorder, 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()))
conductance_averaged += conductance
conductance_averaged = conductance_averaged/calculation_times
return conductance
def calculate_conductance_with_slice_disorder(fermi_energy, h00, h01, disorder_intensity=2.0, disorder_concentration=1.0, length=100):
@@ -2472,6 +2476,29 @@ def plot_contour(x_array, y_array, matrix, xlabel='x', ylabel='y', title='', fon
plt.show()
plt.close('all')
def plot_pcolor(x_array, y_array, matrix, xlabel='x', ylabel='y', title='', fontsize=20, labelsize=15, cmap='jet', levels=None, show=1, save=0, filename='a', file_format='.jpg', dpi=300):
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
plt.subplots_adjust(bottom=0.2, right=0.75, left=0.2)
x_array, y_array = np.meshgrid(x_array, y_array)
contour = ax.pcolor(x_array,y_array,matrix, cmap=cmap)
ax.set_title(title, fontsize=fontsize, fontfamily='Times New Roman')
ax.set_xlabel(xlabel, fontsize=fontsize, fontfamily='Times New Roman')
ax.set_ylabel(ylabel, fontsize=fontsize, fontfamily='Times New Roman')
ax.tick_params(labelsize=labelsize)
labels = ax.get_xticklabels() + ax.get_yticklabels()
[label.set_fontname('Times New Roman') for label in labels]
cax = plt.axes([0.8, 0.2, 0.05, 0.68])
cbar = fig.colorbar(contour, cax=cax)
cbar.ax.tick_params(labelsize=labelsize)
for l in cbar.ax.yaxis.get_ticklabels():
l.set_family('Times New Roman')
if save == 1:
plt.savefig(filename+file_format, dpi=dpi)
if show == 1:
plt.show()
plt.close('all')
def draw_dots_and_lines(coordinate_array, draw_dots=1, draw_lines=1, max_distance=1.1, line_style='-k', linewidth=1, dot_style='ro', markersize=3, show=1, save=0, filename='a', file_format='.eps', dpi=300):
import matplotlib.pyplot as plt
coordinate_array = np.array(coordinate_array)