0.1.75
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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 = 0.1.74
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version = 0.1.75
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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: 0.1.74
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Version: 0.1.75
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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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@ -9,7 +9,10 @@ src/guan/__init__.py
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src/guan/band_structures_and_wave_functions.py
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src/guan/band_structures_and_wave_functions.py
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src/guan/basic_functions.py
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src/guan/basic_functions.py
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src/guan/data_processing.py
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src/guan/data_processing.py
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src/guan/decorator.py
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src/guan/density_of_states.py
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src/guan/density_of_states.py
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src/guan/figure_plotting.py
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src/guan/file_reading_and_writing.py
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src/guan/machine_learning.py
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src/guan/machine_learning.py
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src/guan/others.py
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src/guan/others.py
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src/guan/quantum_transport.py
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src/guan/quantum_transport.py
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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). The primary location of this package is on website https://py.guanjihuan.com. The GitHub location of this package is on https://github.com/guanjihuan/py.guanjihuan.com.
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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). The primary location of this package is on website https://py.guanjihuan.com. The GitHub location of this package is on https://github.com/guanjihuan/py.guanjihuan.com.
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# 函数的装饰器，用于获取计算时间（秒）
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from .decorator import *
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def timer_decorator(func):
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import time
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def wrapper(*args, **kwargs):
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start = time.time()
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result = func(*args, **kwargs)
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end = time.time()
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print(f"Running time of {func.__name__}: {end - start} seconds")
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return result
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return wrapper
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# 函数的装饰器，用于获取计算时间（分）
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def timer_decorator_minutes(func):
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import time
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def wrapper(*args, **kwargs):
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start = time.time()
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result = func(*args, **kwargs)
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end = time.time()
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print(f"Running time of {func.__name__}: {(end - start)/60} minutes")
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return result
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return wrapper
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# 函数的装饰器，用于获取计算时间（时）
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def timer_decorator_hours(func):
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import time
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def wrapper(*args, **kwargs):
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start = time.time()
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result = func(*args, **kwargs)
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end = time.time()
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print(f"Running time of {func.__name__}: {(end - start)/3600} hours")
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return result
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return wrapper
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# 函数的装饰器，用于GUAN软件包的统计
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def statistics_decorator(func):
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def wrapper(*args, **kwargs):
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result = func(*args, **kwargs)
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import guan
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guan.statistics_of_guan_package(func.__name__)
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return result
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return wrapper
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from .basic_functions import *
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from .basic_functions import *
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from .Fourier_transform import *
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from .Fourier_transform import *
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from .Hamiltonian_of_examples import *
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from .Hamiltonian_of_examples import *
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from .quantum_transport import *
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from .quantum_transport import *
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from .topological_invariant import *
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from .topological_invariant import *
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from .machine_learning import *
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from .machine_learning import *
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from .file_reading_and_writing import *
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from .figure_plotting import *
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from .data_processing import *
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from .data_processing import *
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from .others import *
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from .others import *
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# Module: data_processing (including figure-plotting and file-reading/writing)
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# Module: data_processing
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import guan
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import guan
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# 导入plt, fig, ax
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@guan.statistics_decorator
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def import_plt_and_start_fig_ax(adjust_bottom=0.2, adjust_left=0.2, labelsize=20):
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import matplotlib.pyplot as plt
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fig, ax = plt.subplots()
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plt.subplots_adjust(bottom=adjust_bottom, left=adjust_left)
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ax.grid()
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ax.tick_params(labelsize=labelsize)
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labels = ax.get_xticklabels() + ax.get_yticklabels()
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[label.set_fontname('Times New Roman') for label in labels]
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return plt, fig, ax
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# 基于plt, fig, ax画图
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@guan.statistics_decorator
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def plot_without_starting_fig(plt, fig, ax, x_array, y_array, xlabel='x', ylabel='y', title='', fontsize=20, style='', y_min=None, y_max=None, linewidth=None, markersize=None, color=None):
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if color==None:
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ax.plot(x_array, y_array, style, linewidth=linewidth, markersize=markersize)
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else:
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ax.plot(x_array, y_array, style, linewidth=linewidth, markersize=markersize, color=color)
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ax.set_title(title, fontsize=fontsize, fontfamily='Times New Roman')
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ax.set_xlabel(xlabel, fontsize=fontsize, fontfamily='Times New Roman')
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ax.set_ylabel(ylabel, fontsize=fontsize, fontfamily='Times New Roman')
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if y_min!=None or y_max!=None:
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if y_min==None:
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y_min=min(y_array)
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if y_max==None:
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y_max=max(y_array)
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ax.set_ylim(y_min, y_max)
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# 画图
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@guan.statistics_decorator
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def plot(x_array, y_array, xlabel='x', ylabel='y', title='', fontsize=20, labelsize=20, show=1, save=0, filename='a', file_format='.jpg', dpi=300, style='', y_min=None, y_max=None, linewidth=None, markersize=None, adjust_bottom=0.2, adjust_left=0.2):
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import guan
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plt, fig, ax = guan.import_plt_and_start_fig_ax(adjust_bottom=adjust_bottom, adjust_left=adjust_left, labelsize=labelsize)
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ax.plot(x_array, y_array, style, linewidth=linewidth, markersize=markersize)
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ax.set_title(title, fontsize=fontsize, fontfamily='Times New Roman')
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ax.set_xlabel(xlabel, fontsize=fontsize, fontfamily='Times New Roman')
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ax.set_ylabel(ylabel, fontsize=fontsize, fontfamily='Times New Roman')
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if y_min!=None or y_max!=None:
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if y_min==None:
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y_min=min(y_array)
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if y_max==None:
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y_max=max(y_array)
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ax.set_ylim(y_min, y_max)
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if save == 1:
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plt.savefig(filename+file_format, dpi=dpi)
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if show == 1:
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plt.show()
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plt.close('all')
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# 一组横坐标数据，两组纵坐标数据画图
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@guan.statistics_decorator
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def plot_two_array(x_array, y1_array, y2_array, xlabel='x', ylabel='y', title='', fontsize=20, labelsize=20, show=1, save=0, filename='a', file_format='.jpg', dpi=300, style_1='', style_2='', y_min=None, y_max=None, linewidth_1=None, linewidth_2=None, markersize_1=None, markersize_2=None, adjust_bottom=0.2, adjust_left=0.2):
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import guan
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plt, fig, ax = guan.import_plt_and_start_fig_ax(adjust_bottom=adjust_bottom, adjust_left=adjust_left, labelsize=labelsize)
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ax.plot(x_array, y1_array, style_1, linewidth=linewidth_1, markersize=markersize_1)
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ax.plot(x_array, y2_array, style_2, linewidth=linewidth_2, markersize=markersize_2)
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ax.set_title(title, fontsize=fontsize, fontfamily='Times New Roman')
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ax.set_xlabel(xlabel, fontsize=fontsize, fontfamily='Times New Roman')
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ax.set_ylabel(ylabel, fontsize=fontsize, fontfamily='Times New Roman')
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if y_min!=None or y_max!=None:
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if y_min==None:
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y1_min=min(y1_array)
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y2_min=min(y2_array)
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y_min=min([y1_min, y2_min])
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if y_max==None:
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y1_max=max(y1_array)
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y2_max=max(y2_array)
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y_max=max([y1_max, y2_max])
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ax.set_ylim(y_min, y_max)
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if save == 1:
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plt.savefig(filename+file_format, dpi=dpi)
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if show == 1:
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plt.show()
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plt.close('all')
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# 两组横坐标数据，两组纵坐标数据画图
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@guan.statistics_decorator
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def plot_two_array_with_two_horizontal_array(x1_array, x2_array, y1_array, y2_array, xlabel='x', ylabel='y', title='', fontsize=20, labelsize=20, show=1, save=0, filename='a', file_format='.jpg', dpi=300, style_1='', style_2='', y_min=None, y_max=None, linewidth_1=None, linewidth_2=None, markersize_1=None, markersize_2=None, adjust_bottom=0.2, adjust_left=0.2):
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import guan
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plt, fig, ax = guan.import_plt_and_start_fig_ax(adjust_bottom=adjust_bottom, adjust_left=adjust_left, labelsize=labelsize)
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ax.plot(x1_array, y1_array, style_1, linewidth=linewidth_1, markersize=markersize_1)
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ax.plot(x2_array, y2_array, style_2, linewidth=linewidth_2, markersize=markersize_2)
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ax.set_title(title, fontsize=fontsize, fontfamily='Times New Roman')
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ax.set_xlabel(xlabel, fontsize=fontsize, fontfamily='Times New Roman')
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ax.set_ylabel(ylabel, fontsize=fontsize, fontfamily='Times New Roman')
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if y_min!=None or y_max!=None:
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if y_min==None:
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y1_min=min(y1_array)
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y2_min=min(y2_array)
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y_min=min([y1_min, y2_min])
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if y_max==None:
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y1_max=max(y1_array)
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y2_max=max(y2_array)
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y_max=max([y1_max, y2_max])
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ax.set_ylim(y_min, y_max)
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if save == 1:
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plt.savefig(filename+file_format, dpi=dpi)
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if show == 1:
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plt.show()
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plt.close('all')
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# 一组横坐标数据，三组纵坐标数据画图
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@guan.statistics_decorator
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def plot_three_array(x_array, y1_array, y2_array, y3_array, xlabel='x', ylabel='y', title='', fontsize=20, labelsize=20, show=1, save=0, filename='a', file_format='.jpg', dpi=300, style_1='', style_2='', style_3='', y_min=None, y_max=None, linewidth_1=None, linewidth_2=None, linewidth_3=None,markersize_1=None, markersize_2=None, markersize_3=None, adjust_bottom=0.2, adjust_left=0.2):
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import guan
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plt, fig, ax = guan.import_plt_and_start_fig_ax(adjust_bottom=adjust_bottom, adjust_left=adjust_left, labelsize=labelsize)
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ax.plot(x_array, y1_array, style_1, linewidth=linewidth_1, markersize=markersize_1)
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ax.plot(x_array, y2_array, style_2, linewidth=linewidth_2, markersize=markersize_2)
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ax.plot(x_array, y3_array, style_3, linewidth=linewidth_3, markersize=markersize_3)
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ax.set_title(title, fontsize=fontsize, fontfamily='Times New Roman')
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ax.set_xlabel(xlabel, fontsize=fontsize, fontfamily='Times New Roman')
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ax.set_ylabel(ylabel, fontsize=fontsize, fontfamily='Times New Roman')
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if y_min!=None or y_max!=None:
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if y_min==None:
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y1_min=min(y1_array)
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y2_min=min(y2_array)
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y3_min=min(y3_array)
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y_min=min([y1_min, y2_min, y3_min])
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if y_max==None:
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y1_max=max(y1_array)
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y2_max=max(y2_array)
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y3_max=max(y3_array)
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y_max=max([y1_max, y2_max, y3_max])
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ax.set_ylim(y_min, y_max)
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if save == 1:
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plt.savefig(filename+file_format, dpi=dpi)
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if show == 1:
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plt.show()
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plt.close('all')
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# 三组横坐标数据，三组纵坐标数据画图
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@guan.statistics_decorator
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def plot_three_array_with_three_horizontal_array(x1_array, x2_array, x3_array, y1_array, y2_array, y3_array, xlabel='x', ylabel='y', title='', fontsize=20, labelsize=20, show=1, save=0, filename='a', file_format='.jpg', dpi=300, style_1='', style_2='', style_3='', y_min=None, y_max=None, linewidth_1=None, linewidth_2=None, linewidth_3=None,markersize_1=None, markersize_2=None, markersize_3=None, adjust_bottom=0.2, adjust_left=0.2):
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import guan
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plt, fig, ax = guan.import_plt_and_start_fig_ax(adjust_bottom=adjust_bottom, adjust_left=adjust_left, labelsize=labelsize)
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ax.plot(x1_array, y1_array, style_1, linewidth=linewidth_1, markersize=markersize_1)
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ax.plot(x2_array, y2_array, style_2, linewidth=linewidth_2, markersize=markersize_2)
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ax.plot(x3_array, y3_array, style_3, linewidth=linewidth_3, markersize=markersize_3)
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ax.set_title(title, fontsize=fontsize, fontfamily='Times New Roman')
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ax.set_xlabel(xlabel, fontsize=fontsize, fontfamily='Times New Roman')
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ax.set_ylabel(ylabel, fontsize=fontsize, fontfamily='Times New Roman')
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if y_min!=None or y_max!=None:
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if y_min==None:
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y1_min=min(y1_array)
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y2_min=min(y2_array)
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y3_min=min(y3_array)
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y_min=min([y1_min, y2_min, y3_min])
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if y_max==None:
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y1_max=max(y1_array)
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y2_max=max(y2_array)
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y3_max=max(y3_array)
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y_max=max([y1_max, y2_max, y3_max])
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ax.set_ylim(y_min, y_max)
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if save == 1:
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plt.savefig(filename+file_format, dpi=dpi)
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if show == 1:
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plt.show()
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plt.close('all')
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# 画三维图
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@guan.statistics_decorator
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def plot_3d_surface(x_array, y_array, matrix, xlabel='x', ylabel='y', zlabel='z', title='', fontsize=20, labelsize=15, show=1, save=0, filename='a', file_format='.jpg', dpi=300, z_min=None, z_max=None, rcount=100, ccount=100):
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import numpy as np
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import matplotlib.pyplot as plt
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from matplotlib import cm
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from matplotlib.ticker import LinearLocator
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matrix = np.array(matrix)
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fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
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plt.subplots_adjust(bottom=0.1, right=0.65)
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x_array, y_array = np.meshgrid(x_array, y_array)
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if len(matrix.shape) == 2:
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surf = ax.plot_surface(x_array, y_array, matrix, rcount=rcount, ccount=ccount, cmap=cm.coolwarm, linewidth=0, antialiased=False)
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elif len(matrix.shape) == 3:
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for i0 in range(matrix.shape[2]):
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surf = ax.plot_surface(x_array, y_array, matrix[:,:,i0], rcount=rcount, ccount=ccount, cmap=cm.coolwarm, linewidth=0, antialiased=False)
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ax.set_title(title, fontsize=fontsize, fontfamily='Times New Roman')
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ax.set_xlabel(xlabel, fontsize=fontsize, fontfamily='Times New Roman')
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ax.set_ylabel(ylabel, fontsize=fontsize, fontfamily='Times New Roman')
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ax.set_zlabel(zlabel, fontsize=fontsize, fontfamily='Times New Roman')
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|
||||||
ax.zaxis.set_major_locator(LinearLocator(5))
|
|
||||||
ax.zaxis.set_major_formatter('{x:.2f}')
|
|
||||||
if z_min!=None or z_max!=None:
|
|
||||||
if z_min==None:
|
|
||||||
z_min=matrix.min()
|
|
||||||
if z_max==None:
|
|
||||||
z_max=matrix.max()
|
|
||||||
ax.set_zlim(z_min, z_max)
|
|
||||||
ax.tick_params(labelsize=labelsize)
|
|
||||||
labels = ax.get_xticklabels() + ax.get_yticklabels() + ax.get_zticklabels()
|
|
||||||
[label.set_fontname('Times New Roman') for label in labels]
|
|
||||||
cax = plt.axes([0.8, 0.1, 0.05, 0.8])
|
|
||||||
cbar = fig.colorbar(surf, 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')
|
|
||||||
|
|
||||||
# 画Contour图
|
|
||||||
@guan.statistics_decorator
|
|
||||||
def plot_contour(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 numpy as np
|
|
||||||
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.contourf(x_array,y_array,matrix,cmap=cmap, levels=levels)
|
|
||||||
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')
|
|
||||||
|
|
||||||
# 画棋盘图/伪彩色图
|
|
||||||
@guan.statistics_decorator
|
|
||||||
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 numpy as np
|
|
||||||
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')
|
|
||||||
|
|
||||||
# 通过坐标画点和线
|
|
||||||
@guan.statistics_decorator
|
|
||||||
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 numpy as np
|
|
||||||
import matplotlib.pyplot as plt
|
|
||||||
coordinate_array = np.array(coordinate_array)
|
|
||||||
print(coordinate_array.shape)
|
|
||||||
x_range = max(coordinate_array[:, 0])-min(coordinate_array[:, 0])
|
|
||||||
y_range = max(coordinate_array[:, 1])-min(coordinate_array[:, 1])
|
|
||||||
fig, ax = plt.subplots(figsize=(6*x_range/y_range,6))
|
|
||||||
plt.subplots_adjust(left=0, bottom=0, right=1, top=1)
|
|
||||||
plt.axis('off')
|
|
||||||
if draw_lines==1:
|
|
||||||
for i1 in range(coordinate_array.shape[0]):
|
|
||||||
for i2 in range(coordinate_array.shape[0]):
|
|
||||||
if np.sqrt((coordinate_array[i1, 0] - coordinate_array[i2, 0])**2+(coordinate_array[i1, 1] - coordinate_array[i2, 1])**2) < max_distance:
|
|
||||||
ax.plot([coordinate_array[i1, 0], coordinate_array[i2, 0]], [coordinate_array[i1, 1], coordinate_array[i2, 1]], line_style, linewidth=linewidth)
|
|
||||||
if draw_dots==1:
|
|
||||||
for i in range(coordinate_array.shape[0]):
|
|
||||||
ax.plot(coordinate_array[i, 0], coordinate_array[i, 1], dot_style, markersize=markersize)
|
|
||||||
if show==1:
|
|
||||||
plt.show()
|
|
||||||
if save==1:
|
|
||||||
if file_format=='.eps':
|
|
||||||
plt.savefig(filename+file_format)
|
|
||||||
else:
|
|
||||||
plt.savefig(filename+file_format, dpi=dpi)
|
|
||||||
|
|
||||||
# 合并两个图片
|
|
||||||
@guan.statistics_decorator
|
|
||||||
def combine_two_images(image_path_array, figsize=(16,8), show=0, save=1, filename='a', file_format='.jpg', dpi=300):
|
|
||||||
import numpy as np
|
|
||||||
num = np.array(image_path_array).shape[0]
|
|
||||||
if num != 2:
|
|
||||||
print('Error: The number of images should be two!')
|
|
||||||
else:
|
|
||||||
import matplotlib.pyplot as plt
|
|
||||||
import matplotlib.image as mpimg
|
|
||||||
fig = plt.figure(figsize=figsize)
|
|
||||||
plt.subplots_adjust(left=0, right=1, bottom=0, top=1, wspace=0, hspace=0)
|
|
||||||
ax1 = fig.add_subplot(121)
|
|
||||||
ax2 = fig.add_subplot(122)
|
|
||||||
image_1 = mpimg.imread(image_path_array[0])
|
|
||||||
image_2 = mpimg.imread(image_path_array[1])
|
|
||||||
ax1.imshow(image_1)
|
|
||||||
ax2.imshow(image_2)
|
|
||||||
ax1.axis('off')
|
|
||||||
ax2.axis('off')
|
|
||||||
if show == 1:
|
|
||||||
plt.show()
|
|
||||||
if save == 1:
|
|
||||||
plt.savefig(filename+file_format, dpi=dpi)
|
|
||||||
plt.close('all')
|
|
||||||
|
|
||||||
# 合并三个图片
|
|
||||||
@guan.statistics_decorator
|
|
||||||
def combine_three_images(image_path_array, figsize=(16,5), show=0, save=1, filename='a', file_format='.jpg', dpi=300):
|
|
||||||
import numpy as np
|
|
||||||
num = np.array(image_path_array).shape[0]
|
|
||||||
if num != 3:
|
|
||||||
print('Error: The number of images should be three!')
|
|
||||||
else:
|
|
||||||
import matplotlib.pyplot as plt
|
|
||||||
import matplotlib.image as mpimg
|
|
||||||
fig = plt.figure(figsize=figsize)
|
|
||||||
plt.subplots_adjust(left=0, right=1, bottom=0, top=1, wspace=0, hspace=0)
|
|
||||||
ax1 = fig.add_subplot(131)
|
|
||||||
ax2 = fig.add_subplot(132)
|
|
||||||
ax3 = fig.add_subplot(133)
|
|
||||||
image_1 = mpimg.imread(image_path_array[0])
|
|
||||||
image_2 = mpimg.imread(image_path_array[1])
|
|
||||||
image_3 = mpimg.imread(image_path_array[2])
|
|
||||||
ax1.imshow(image_1)
|
|
||||||
ax2.imshow(image_2)
|
|
||||||
ax3.imshow(image_3)
|
|
||||||
ax1.axis('off')
|
|
||||||
ax2.axis('off')
|
|
||||||
ax3.axis('off')
|
|
||||||
if show == 1:
|
|
||||||
plt.show()
|
|
||||||
if save == 1:
|
|
||||||
plt.savefig(filename+file_format, dpi=dpi)
|
|
||||||
plt.close('all')
|
|
||||||
|
|
||||||
# 合并四个图片
|
|
||||||
@guan.statistics_decorator
|
|
||||||
def combine_four_images(image_path_array, figsize=(16,16), show=0, save=1, filename='a', file_format='.jpg', dpi=300):
|
|
||||||
import numpy as np
|
|
||||||
num = np.array(image_path_array).shape[0]
|
|
||||||
if num != 4:
|
|
||||||
print('Error: The number of images should be four!')
|
|
||||||
else:
|
|
||||||
import matplotlib.pyplot as plt
|
|
||||||
import matplotlib.image as mpimg
|
|
||||||
fig = plt.figure(figsize=figsize)
|
|
||||||
plt.subplots_adjust(left=0, right=1, bottom=0, top=1, wspace=0, hspace=0)
|
|
||||||
ax1 = fig.add_subplot(221)
|
|
||||||
ax2 = fig.add_subplot(222)
|
|
||||||
ax3 = fig.add_subplot(223)
|
|
||||||
ax4 = fig.add_subplot(224)
|
|
||||||
image_1 = mpimg.imread(image_path_array[0])
|
|
||||||
image_2 = mpimg.imread(image_path_array[1])
|
|
||||||
image_3 = mpimg.imread(image_path_array[2])
|
|
||||||
image_4 = mpimg.imread(image_path_array[3])
|
|
||||||
ax1.imshow(image_1)
|
|
||||||
ax2.imshow(image_2)
|
|
||||||
ax3.imshow(image_3)
|
|
||||||
ax4.imshow(image_4)
|
|
||||||
ax1.axis('off')
|
|
||||||
ax2.axis('off')
|
|
||||||
ax3.axis('off')
|
|
||||||
ax4.axis('off')
|
|
||||||
if show == 1:
|
|
||||||
plt.show()
|
|
||||||
if save == 1:
|
|
||||||
plt.savefig(filename+file_format, dpi=dpi)
|
|
||||||
plt.close('all')
|
|
||||||
|
|
||||||
# 对某个目录中的txt文件批量读取和画图
|
|
||||||
@guan.statistics_decorator
|
|
||||||
def batch_reading_and_plotting(directory, xlabel='x', ylabel='y'):
|
|
||||||
import re
|
|
||||||
import os
|
|
||||||
import guan
|
|
||||||
for root, dirs, files in os.walk(directory):
|
|
||||||
for file in files:
|
|
||||||
if re.search('^txt.', file[::-1]):
|
|
||||||
filename = file[:-4]
|
|
||||||
x_array, y_array = guan.read_one_dimensional_data(filename=filename)
|
|
||||||
guan.plot(x_array, y_array, xlabel=xlabel, ylabel=ylabel, title=filename, show=0, save=1, filename=filename)
|
|
||||||
|
|
||||||
# 将图片制作GIF动画
|
|
||||||
@guan.statistics_decorator
|
|
||||||
def make_gif(image_path_array, filename='a', duration=0.1):
|
|
||||||
import imageio
|
|
||||||
images = []
|
|
||||||
for image_path in image_path_array:
|
|
||||||
im = imageio.imread(image_path)
|
|
||||||
images.append(im)
|
|
||||||
imageio.mimsave(filename+'.gif', images, 'GIF', duration=duration)
|
|
||||||
|
|
||||||
# 选取Matplotlib颜色
|
|
||||||
@guan.statistics_decorator
|
|
||||||
def color_matplotlib():
|
|
||||||
color_array = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan']
|
|
||||||
return color_array
|
|
||||||
|
|
||||||
# 如果不存在文件夹，则新建文件夹
|
|
||||||
@guan.statistics_decorator
|
|
||||||
def make_directory(directory='./test'):
|
|
||||||
import os
|
|
||||||
if not os.path.exists(directory):
|
|
||||||
os.makedirs(directory)
|
|
||||||
|
|
||||||
# 如果不存在文件，则新建空文件
|
|
||||||
@guan.statistics_decorator
|
|
||||||
def make_file(file_path='./a.txt'):
|
|
||||||
import os
|
|
||||||
if not os.path.exists(file_path):
|
|
||||||
with open(file_path, 'w') as f:
|
|
||||||
pass
|
|
||||||
|
|
||||||
# 读取文本文件内容，如果不存在，则新建空文件
|
|
||||||
@guan.statistics_decorator
|
|
||||||
def read_text_file(file_path='./a.txt'):
|
|
||||||
import os
|
|
||||||
if not os.path.exists(file_path):
|
|
||||||
with open(file_path, 'w') as f:
|
|
||||||
pass
|
|
||||||
return ''
|
|
||||||
else:
|
|
||||||
with open(file_path, 'r') as f:
|
|
||||||
content = f.read()
|
|
||||||
return content
|
|
||||||
|
|
||||||
# 将变量存到文件
|
|
||||||
@guan.statistics_decorator
|
|
||||||
def dump_data(data, filename, file_format='.txt'):
|
|
||||||
import pickle
|
|
||||||
with open(filename+file_format, 'wb') as f:
|
|
||||||
pickle.dump(data, f)
|
|
||||||
|
|
||||||
# 从文件中恢复数据到变量
|
|
||||||
@guan.statistics_decorator
|
|
||||||
def load_data(filename, file_format='.txt'):
|
|
||||||
import pickle
|
|
||||||
with open(filename+file_format, 'rb') as f:
|
|
||||||
data = pickle.load(f)
|
|
||||||
return data
|
|
||||||
|
|
||||||
# 读取文件中的一维数据（一行一组x和y）
|
|
||||||
@guan.statistics_decorator
|
|
||||||
def read_one_dimensional_data(filename='a', file_format='.txt'):
|
|
||||||
import numpy as np
|
|
||||||
f = open(filename+file_format, 'r')
|
|
||||||
text = f.read()
|
|
||||||
f.close()
|
|
||||||
row_list = np.array(text.split('\n'))
|
|
||||||
dim_column = np.array(row_list[0].split()).shape[0]
|
|
||||||
x_array = np.array([])
|
|
||||||
y_array = np.array([])
|
|
||||||
for row in row_list:
|
|
||||||
column = np.array(row.split())
|
|
||||||
if column.shape[0] != 0:
|
|
||||||
x_array = np.append(x_array, [float(column[0])], axis=0)
|
|
||||||
y_row = np.zeros(dim_column-1)
|
|
||||||
for dim0 in range(dim_column-1):
|
|
||||||
y_row[dim0] = float(column[dim0+1])
|
|
||||||
if np.array(y_array).shape[0] == 0:
|
|
||||||
y_array = [y_row]
|
|
||||||
else:
|
|
||||||
y_array = np.append(y_array, [y_row], axis=0)
|
|
||||||
return x_array, y_array
|
|
||||||
|
|
||||||
# 读取文件中的一维数据（一行一组x和y）（支持复数形式）
|
|
||||||
@guan.statistics_decorator
|
|
||||||
def read_one_dimensional_complex_data(filename='a', file_format='.txt'):
|
|
||||||
import numpy as np
|
|
||||||
f = open(filename+file_format, 'r')
|
|
||||||
text = f.read()
|
|
||||||
f.close()
|
|
||||||
row_list = np.array(text.split('\n'))
|
|
||||||
dim_column = np.array(row_list[0].split()).shape[0]
|
|
||||||
x_array = np.array([])
|
|
||||||
y_array = np.array([])
|
|
||||||
for row in row_list:
|
|
||||||
column = np.array(row.split())
|
|
||||||
if column.shape[0] != 0:
|
|
||||||
x_array = np.append(x_array, [complex(column[0])], axis=0)
|
|
||||||
y_row = np.zeros(dim_column-1, dtype=complex)
|
|
||||||
for dim0 in range(dim_column-1):
|
|
||||||
y_row[dim0] = complex(column[dim0+1])
|
|
||||||
if np.array(y_array).shape[0] == 0:
|
|
||||||
y_array = [y_row]
|
|
||||||
else:
|
|
||||||
y_array = np.append(y_array, [y_row], axis=0)
|
|
||||||
return x_array, y_array
|
|
||||||
|
|
||||||
# 读取文件中的二维数据（第一行和第一列分别为横纵坐标）
|
|
||||||
@guan.statistics_decorator
|
|
||||||
def read_two_dimensional_data(filename='a', file_format='.txt'):
|
|
||||||
import numpy as np
|
|
||||||
f = open(filename+file_format, 'r')
|
|
||||||
text = f.read()
|
|
||||||
f.close()
|
|
||||||
row_list = np.array(text.split('\n'))
|
|
||||||
dim_column = np.array(row_list[0].split()).shape[0]
|
|
||||||
x_array = np.array([])
|
|
||||||
y_array = np.array([])
|
|
||||||
matrix = np.array([])
|
|
||||||
for i0 in range(row_list.shape[0]):
|
|
||||||
column = np.array(row_list[i0].split())
|
|
||||||
if i0 == 0:
|
|
||||||
x_str = column[1::]
|
|
||||||
x_array = np.zeros(x_str.shape[0])
|
|
||||||
for i00 in range(x_str.shape[0]):
|
|
||||||
x_array[i00] = float(x_str[i00])
|
|
||||||
elif column.shape[0] != 0:
|
|
||||||
y_array = np.append(y_array, [float(column[0])], axis=0)
|
|
||||||
matrix_row = np.zeros(dim_column-1)
|
|
||||||
for dim0 in range(dim_column-1):
|
|
||||||
matrix_row[dim0] = float(column[dim0+1])
|
|
||||||
if np.array(matrix).shape[0] == 0:
|
|
||||||
matrix = [matrix_row]
|
|
||||||
else:
|
|
||||||
matrix = np.append(matrix, [matrix_row], axis=0)
|
|
||||||
return x_array, y_array, matrix
|
|
||||||
|
|
||||||
# 读取文件中的二维数据（第一行和第一列分别为横纵坐标）（支持复数形式）
|
|
||||||
@guan.statistics_decorator
|
|
||||||
def read_two_dimensional_complex_data(filename='a', file_format='.txt'):
|
|
||||||
import numpy as np
|
|
||||||
f = open(filename+file_format, 'r')
|
|
||||||
text = f.read()
|
|
||||||
f.close()
|
|
||||||
row_list = np.array(text.split('\n'))
|
|
||||||
dim_column = np.array(row_list[0].split()).shape[0]
|
|
||||||
x_array = np.array([])
|
|
||||||
y_array = np.array([])
|
|
||||||
matrix = np.array([])
|
|
||||||
for i0 in range(row_list.shape[0]):
|
|
||||||
column = np.array(row_list[i0].split())
|
|
||||||
if i0 == 0:
|
|
||||||
x_str = column[1::]
|
|
||||||
x_array = np.zeros(x_str.shape[0], dtype=complex)
|
|
||||||
for i00 in range(x_str.shape[0]):
|
|
||||||
x_array[i00] = complex(x_str[i00])
|
|
||||||
elif column.shape[0] != 0:
|
|
||||||
y_array = np.append(y_array, [complex(column[0])], axis=0)
|
|
||||||
matrix_row = np.zeros(dim_column-1, dtype=complex)
|
|
||||||
for dim0 in range(dim_column-1):
|
|
||||||
matrix_row[dim0] = complex(column[dim0+1])
|
|
||||||
if np.array(matrix).shape[0] == 0:
|
|
||||||
matrix = [matrix_row]
|
|
||||||
else:
|
|
||||||
matrix = np.append(matrix, [matrix_row], axis=0)
|
|
||||||
return x_array, y_array, matrix
|
|
||||||
|
|
||||||
# 读取文件中的二维数据（不包括x和y）
|
|
||||||
@guan.statistics_decorator
|
|
||||||
def read_two_dimensional_data_without_xy_array(filename='a', file_format='.txt'):
|
|
||||||
import numpy as np
|
|
||||||
matrix = np.loadtxt(filename+file_format)
|
|
||||||
return matrix
|
|
||||||
|
|
||||||
# 打开文件用于新增内容
|
|
||||||
@guan.statistics_decorator
|
|
||||||
def open_file(filename='a', file_format='.txt'):
|
|
||||||
f = open(filename+file_format, 'a', encoding='UTF-8')
|
|
||||||
return f
|
|
||||||
|
|
||||||
# 在文件中写入一维数据（一行一组x和y）
|
|
||||||
@guan.statistics_decorator
|
|
||||||
def write_one_dimensional_data(x_array, y_array, filename='a', file_format='.txt'):
|
|
||||||
import guan
|
|
||||||
with open(filename+file_format, 'w', encoding='UTF-8') as f:
|
|
||||||
guan.write_one_dimensional_data_without_opening_file(x_array, y_array, f)
|
|
||||||
|
|
||||||
# 在文件中写入一维数据（一行一组x和y）（需要输入已打开的文件）
|
|
||||||
@guan.statistics_decorator
|
|
||||||
def write_one_dimensional_data_without_opening_file(x_array, y_array, f):
|
|
||||||
import numpy as np
|
|
||||||
x_array = np.array(x_array)
|
|
||||||
y_array = np.array(y_array)
|
|
||||||
i0 = 0
|
|
||||||
for x0 in x_array:
|
|
||||||
f.write(str(x0)+' ')
|
|
||||||
if len(y_array.shape) == 1:
|
|
||||||
f.write(str(y_array[i0])+'\n')
|
|
||||||
elif len(y_array.shape) == 2:
|
|
||||||
for j0 in range(y_array.shape[1]):
|
|
||||||
f.write(str(y_array[i0, j0])+' ')
|
|
||||||
f.write('\n')
|
|
||||||
i0 += 1
|
|
||||||
|
|
||||||
# 在文件中写入二维数据（第一行和第一列分别为横纵坐标）
|
|
||||||
@guan.statistics_decorator
|
|
||||||
def write_two_dimensional_data(x_array, y_array, matrix, filename='a', file_format='.txt'):
|
|
||||||
import guan
|
|
||||||
with open(filename+file_format, 'w', encoding='UTF-8') as f:
|
|
||||||
guan.write_two_dimensional_data_without_opening_file(x_array, y_array, matrix, f)
|
|
||||||
|
|
||||||
# 在文件中写入二维数据（第一行和第一列分别为横纵坐标）（需要输入已打开的文件）
|
|
||||||
@guan.statistics_decorator
|
|
||||||
def write_two_dimensional_data_without_opening_file(x_array, y_array, matrix, f):
|
|
||||||
import numpy as np
|
|
||||||
x_array = np.array(x_array)
|
|
||||||
y_array = np.array(y_array)
|
|
||||||
matrix = np.array(matrix)
|
|
||||||
f.write('0 ')
|
|
||||||
for x0 in x_array:
|
|
||||||
f.write(str(x0)+' ')
|
|
||||||
f.write('\n')
|
|
||||||
i0 = 0
|
|
||||||
for y0 in y_array:
|
|
||||||
f.write(str(y0))
|
|
||||||
j0 = 0
|
|
||||||
for x0 in x_array:
|
|
||||||
f.write(' '+str(matrix[i0, j0])+' ')
|
|
||||||
j0 += 1
|
|
||||||
f.write('\n')
|
|
||||||
i0 += 1
|
|
||||||
|
|
||||||
# 在文件中写入二维数据（不包括x和y）
|
|
||||||
@guan.statistics_decorator
|
|
||||||
def write_two_dimensional_data_without_xy_array(matrix, filename='a', file_format='.txt'):
|
|
||||||
import guan
|
|
||||||
with open(filename+file_format, 'w', encoding='UTF-8') as f:
|
|
||||||
guan.write_two_dimensional_data_without_xy_array_and_without_opening_file(matrix, f)
|
|
||||||
|
|
||||||
# 在文件中写入二维数据（不包括x和y）（需要输入已打开的文件）
|
|
||||||
@guan.statistics_decorator
|
|
||||||
def write_two_dimensional_data_without_xy_array_and_without_opening_file(matrix, f):
|
|
||||||
for row in matrix:
|
|
||||||
for element in row:
|
|
||||||
f.write(str(element)+' ')
|
|
||||||
f.write('\n')
|
|
||||||
import guan
|
|
||||||
|
|
||||||
# 以显示编号的样式，打印数组
|
|
||||||
@guan.statistics_decorator
|
|
||||||
def print_array_with_index(array, show_index=1, index_type=0):
|
|
||||||
if show_index==0:
|
|
||||||
for i0 in array:
|
|
||||||
print(i0)
|
|
||||||
else:
|
|
||||||
if index_type==0:
|
|
||||||
index = 0
|
|
||||||
for i0 in array:
|
|
||||||
print(index, i0)
|
|
||||||
index += 1
|
|
||||||
else:
|
|
||||||
index = 0
|
|
||||||
for i0 in array:
|
|
||||||
index += 1
|
|
||||||
print(index, i0)
|
|
||||||
|
|
||||||
# 获取目录中的所有文件名
|
|
||||||
@guan.statistics_decorator
|
|
||||||
def get_all_filenames_in_directory(directory='./', file_format=None):
|
|
||||||
import os
|
|
||||||
file_list = []
|
|
||||||
for root, dirs, files in os.walk(directory):
|
|
||||||
for i0 in range(len(files)):
|
|
||||||
if file_format == None:
|
|
||||||
file_list.append(files[i0])
|
|
||||||
else:
|
|
||||||
if file_format in files[i0]:
|
|
||||||
file_list.append(files[i0])
|
|
||||||
return file_list
|
|
||||||
|
|
||||||
# 获取目录中的所有文件名（不包括子目录）
|
|
||||||
@guan.statistics_decorator
|
|
||||||
def get_all_filenames_in_directory_without_subdirectory(directory='./', file_format=None):
|
|
||||||
import os
|
|
||||||
file_list = []
|
|
||||||
for root, dirs, files in os.walk(directory):
|
|
||||||
for i0 in range(len(files)):
|
|
||||||
if file_format == None:
|
|
||||||
file_list.append(files[i0])
|
|
||||||
else:
|
|
||||||
if file_format in files[i0]:
|
|
||||||
file_list.append(files[i0])
|
|
||||||
break
|
|
||||||
return file_list
|
|
||||||
|
|
||||||
# 获取文件夹中某种文本类型的文件路径以及读取内容
|
|
||||||
@guan.statistics_decorator
|
|
||||||
def read_text_files_in_directory(directory='./', file_format='.md'):
|
|
||||||
import os
|
|
||||||
file_list = []
|
|
||||||
for root, dirs, files in os.walk(directory):
|
|
||||||
for i0 in range(len(files)):
|
|
||||||
if file_format in files[i0]:
|
|
||||||
file_list.append(root+'/'+files[i0])
|
|
||||||
content_array = []
|
|
||||||
for file in file_list:
|
|
||||||
with open(file, 'r') as f:
|
|
||||||
content_array.append(f.read())
|
|
||||||
return file_list, content_array
|
|
||||||
|
|
||||||
# 在多个文本文件中查找关键词
|
|
||||||
@guan.statistics_decorator
|
|
||||||
def find_words_in_multiple_files(words, directory='./', file_format='.md'):
|
|
||||||
import guan
|
|
||||||
file_list, content_array = guan.read_text_files_in_directory(directory=directory, file_format=file_format)
|
|
||||||
num_files = len(file_list)
|
|
||||||
file_list_with_words = []
|
|
||||||
for i0 in range(num_files):
|
|
||||||
if words in content_array[i0]:
|
|
||||||
file_list_with_words.append(file_list[i0])
|
|
||||||
return file_list_with_words
|
|
||||||
|
|
||||||
# 并行计算前的预处理，把参数分成多份
|
# 并行计算前的预处理，把参数分成多份
|
||||||
@guan.statistics_decorator
|
@guan.statistics_decorator
|
||||||
def preprocess_for_parallel_calculations(parameter_array_all, cpus=1, task_index=0):
|
def preprocess_for_parallel_calculations(parameter_array_all, cpus=1, task_index=0):
|
||||||
@ -745,6 +38,24 @@ def generate_random_int_number_for_a_specific_seed(seed=0, x_min=0, x_max=10):
|
|||||||
rand_num = np.random.randint(x_min, x_max) # 左闭右开[x_min, x_max)
|
rand_num = np.random.randint(x_min, x_max) # 左闭右开[x_min, x_max)
|
||||||
return rand_num
|
return rand_num
|
||||||
|
|
||||||
|
# 以显示编号的样式，打印数组
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def print_array_with_index(array, show_index=1, index_type=0):
|
||||||
|
if show_index==0:
|
||||||
|
for i0 in array:
|
||||||
|
print(i0)
|
||||||
|
else:
|
||||||
|
if index_type==0:
|
||||||
|
index = 0
|
||||||
|
for i0 in array:
|
||||||
|
print(index, i0)
|
||||||
|
index += 1
|
||||||
|
else:
|
||||||
|
index = 0
|
||||||
|
for i0 in array:
|
||||||
|
index += 1
|
||||||
|
print(index, i0)
|
||||||
|
|
||||||
# 使用jieba软件包进行分词
|
# 使用jieba软件包进行分词
|
||||||
@guan.statistics_decorator
|
@guan.statistics_decorator
|
||||||
def divide_text_into_words(text):
|
def divide_text_into_words(text):
|
||||||
@ -752,18 +63,79 @@ def divide_text_into_words(text):
|
|||||||
words = jieba.lcut(text)
|
words = jieba.lcut(text)
|
||||||
return words
|
return words
|
||||||
|
|
||||||
# 拼接两个PDF文件
|
# 根据一定的字符长度来分割文本
|
||||||
@guan.statistics_decorator
|
@guan.statistics_decorator
|
||||||
def combine_two_pdf_files(input_file_1='a.pdf', input_file_2='b.pdf', output_file='combined_file.pdf'):
|
def split_text(text, wrap_width=3000):
|
||||||
import PyPDF2
|
import textwrap
|
||||||
output_pdf = PyPDF2.PdfWriter()
|
split_text_list = textwrap.wrap(text, wrap_width)
|
||||||
with open(input_file_1, 'rb') as file1:
|
return split_text_list
|
||||||
pdf1 = PyPDF2.PdfReader(file1)
|
|
||||||
for page in range(len(pdf1.pages)):
|
# 判断某个字符是中文还是英文或其他
|
||||||
output_pdf.add_page(pdf1.pages[page])
|
@guan.statistics_decorator
|
||||||
with open(input_file_2, 'rb') as file2:
|
def check_Chinese_or_English(a):
|
||||||
pdf2 = PyPDF2.PdfReader(file2)
|
if '\u4e00' <= a <= '\u9fff' :
|
||||||
for page in range(len(pdf2.pages)):
|
word_type = 'Chinese'
|
||||||
output_pdf.add_page(pdf2.pages[page])
|
elif '\x00' <= a <= '\xff':
|
||||||
with open(output_file, 'wb') as combined_file:
|
word_type = 'English'
|
||||||
output_pdf.write(combined_file)
|
else:
|
||||||
|
word_type = 'Others'
|
||||||
|
return word_type
|
||||||
|
|
||||||
|
# 统计中英文文本的字数，默认不包括空格
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def count_words(text, include_space=0, show_words=0):
|
||||||
|
import jieba
|
||||||
|
import guan
|
||||||
|
words = jieba.lcut(text)
|
||||||
|
new_words = []
|
||||||
|
if include_space == 0:
|
||||||
|
for word in words:
|
||||||
|
if word != ' ':
|
||||||
|
new_words.append(word)
|
||||||
|
else:
|
||||||
|
new_words = words
|
||||||
|
num_words = 0
|
||||||
|
new_words_2 = []
|
||||||
|
for word in new_words:
|
||||||
|
word_type = guan.check_Chinese_or_English(word[0])
|
||||||
|
if word_type == 'Chinese':
|
||||||
|
num_words += len(word)
|
||||||
|
for one_word in word:
|
||||||
|
new_words_2.append(one_word)
|
||||||
|
elif word_type == 'English' or 'Others':
|
||||||
|
num_words += 1
|
||||||
|
new_words_2.append(word)
|
||||||
|
if show_words == 1:
|
||||||
|
print(new_words_2)
|
||||||
|
return num_words
|
||||||
|
|
||||||
|
# 将RGB转成HEX
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def rgb_to_hex(rgb, pound=1):
|
||||||
|
if pound==0:
|
||||||
|
return '%02x%02x%02x' % rgb
|
||||||
|
else:
|
||||||
|
return '#%02x%02x%02x' % rgb
|
||||||
|
|
||||||
|
# 将HEX转成RGB
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def hex_to_rgb(hex):
|
||||||
|
hex = hex.lstrip('#')
|
||||||
|
length = len(hex)
|
||||||
|
return tuple(int(hex[i:i+length//3], 16) for i in range(0, length, length//3))
|
||||||
|
|
||||||
|
# 使用MD5进行散列加密
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def encryption_MD5(password, salt=''):
|
||||||
|
import hashlib
|
||||||
|
password = salt+password
|
||||||
|
hashed_password = hashlib.md5(password.encode()).hexdigest()
|
||||||
|
return hashed_password
|
||||||
|
|
||||||
|
# 使用SHA-256进行散列加密
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def encryption_SHA_256(password, salt=''):
|
||||||
|
import hashlib
|
||||||
|
password = salt+password
|
||||||
|
hashed_password = hashlib.sha256(password.encode()).hexdigest()
|
||||||
|
return hashed_password
|
||||||
41
PyPI/src/guan/decorator.py
Normal file
41
PyPI/src/guan/decorator.py
Normal file
@ -0,0 +1,41 @@
|
|||||||
|
# 函数的装饰器，用于获取计算时间（秒）
|
||||||
|
def timer_decorator(func):
|
||||||
|
import time
|
||||||
|
def wrapper(*args, **kwargs):
|
||||||
|
start = time.time()
|
||||||
|
result = func(*args, **kwargs)
|
||||||
|
end = time.time()
|
||||||
|
print(f"Running time of {func.__name__}: {end - start} seconds")
|
||||||
|
return result
|
||||||
|
return wrapper
|
||||||
|
|
||||||
|
# 函数的装饰器，用于获取计算时间（分）
|
||||||
|
def timer_decorator_minutes(func):
|
||||||
|
import time
|
||||||
|
def wrapper(*args, **kwargs):
|
||||||
|
start = time.time()
|
||||||
|
result = func(*args, **kwargs)
|
||||||
|
end = time.time()
|
||||||
|
print(f"Running time of {func.__name__}: {(end - start)/60} minutes")
|
||||||
|
return result
|
||||||
|
return wrapper
|
||||||
|
|
||||||
|
# 函数的装饰器，用于获取计算时间（时）
|
||||||
|
def timer_decorator_hours(func):
|
||||||
|
import time
|
||||||
|
def wrapper(*args, **kwargs):
|
||||||
|
start = time.time()
|
||||||
|
result = func(*args, **kwargs)
|
||||||
|
end = time.time()
|
||||||
|
print(f"Running time of {func.__name__}: {(end - start)/3600} hours")
|
||||||
|
return result
|
||||||
|
return wrapper
|
||||||
|
|
||||||
|
# 函数的装饰器，用于GUAN软件包的统计
|
||||||
|
def statistics_decorator(func):
|
||||||
|
def wrapper(*args, **kwargs):
|
||||||
|
result = func(*args, **kwargs)
|
||||||
|
import guan
|
||||||
|
guan.statistics_of_guan_package(func.__name__)
|
||||||
|
return result
|
||||||
|
return wrapper
|
||||||
403
PyPI/src/guan/figure_plotting.py
Normal file
403
PyPI/src/guan/figure_plotting.py
Normal file
@ -0,0 +1,403 @@
|
|||||||
|
# Module: figure_plotting
|
||||||
|
import guan
|
||||||
|
|
||||||
|
# 导入plt, fig, ax
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def import_plt_and_start_fig_ax(adjust_bottom=0.2, adjust_left=0.2, labelsize=20):
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
fig, ax = plt.subplots()
|
||||||
|
plt.subplots_adjust(bottom=adjust_bottom, left=adjust_left)
|
||||||
|
ax.grid()
|
||||||
|
ax.tick_params(labelsize=labelsize)
|
||||||
|
labels = ax.get_xticklabels() + ax.get_yticklabels()
|
||||||
|
[label.set_fontname('Times New Roman') for label in labels]
|
||||||
|
return plt, fig, ax
|
||||||
|
|
||||||
|
# 基于plt, fig, ax画图
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def plot_without_starting_fig(plt, fig, ax, x_array, y_array, xlabel='x', ylabel='y', title='', fontsize=20, style='', y_min=None, y_max=None, linewidth=None, markersize=None, color=None):
|
||||||
|
if color==None:
|
||||||
|
ax.plot(x_array, y_array, style, linewidth=linewidth, markersize=markersize)
|
||||||
|
else:
|
||||||
|
ax.plot(x_array, y_array, style, linewidth=linewidth, markersize=markersize, color=color)
|
||||||
|
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')
|
||||||
|
if y_min!=None or y_max!=None:
|
||||||
|
if y_min==None:
|
||||||
|
y_min=min(y_array)
|
||||||
|
if y_max==None:
|
||||||
|
y_max=max(y_array)
|
||||||
|
ax.set_ylim(y_min, y_max)
|
||||||
|
|
||||||
|
# 画图
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def plot(x_array, y_array, xlabel='x', ylabel='y', title='', fontsize=20, labelsize=20, show=1, save=0, filename='a', file_format='.jpg', dpi=300, style='', y_min=None, y_max=None, linewidth=None, markersize=None, adjust_bottom=0.2, adjust_left=0.2):
|
||||||
|
import guan
|
||||||
|
plt, fig, ax = guan.import_plt_and_start_fig_ax(adjust_bottom=adjust_bottom, adjust_left=adjust_left, labelsize=labelsize)
|
||||||
|
ax.plot(x_array, y_array, style, linewidth=linewidth, markersize=markersize)
|
||||||
|
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')
|
||||||
|
if y_min!=None or y_max!=None:
|
||||||
|
if y_min==None:
|
||||||
|
y_min=min(y_array)
|
||||||
|
if y_max==None:
|
||||||
|
y_max=max(y_array)
|
||||||
|
ax.set_ylim(y_min, y_max)
|
||||||
|
if save == 1:
|
||||||
|
plt.savefig(filename+file_format, dpi=dpi)
|
||||||
|
if show == 1:
|
||||||
|
plt.show()
|
||||||
|
plt.close('all')
|
||||||
|
|
||||||
|
# 一组横坐标数据，两组纵坐标数据画图
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def plot_two_array(x_array, y1_array, y2_array, xlabel='x', ylabel='y', title='', fontsize=20, labelsize=20, show=1, save=0, filename='a', file_format='.jpg', dpi=300, style_1='', style_2='', y_min=None, y_max=None, linewidth_1=None, linewidth_2=None, markersize_1=None, markersize_2=None, adjust_bottom=0.2, adjust_left=0.2):
|
||||||
|
import guan
|
||||||
|
plt, fig, ax = guan.import_plt_and_start_fig_ax(adjust_bottom=adjust_bottom, adjust_left=adjust_left, labelsize=labelsize)
|
||||||
|
ax.plot(x_array, y1_array, style_1, linewidth=linewidth_1, markersize=markersize_1)
|
||||||
|
ax.plot(x_array, y2_array, style_2, linewidth=linewidth_2, markersize=markersize_2)
|
||||||
|
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')
|
||||||
|
if y_min!=None or y_max!=None:
|
||||||
|
if y_min==None:
|
||||||
|
y1_min=min(y1_array)
|
||||||
|
y2_min=min(y2_array)
|
||||||
|
y_min=min([y1_min, y2_min])
|
||||||
|
if y_max==None:
|
||||||
|
y1_max=max(y1_array)
|
||||||
|
y2_max=max(y2_array)
|
||||||
|
y_max=max([y1_max, y2_max])
|
||||||
|
ax.set_ylim(y_min, y_max)
|
||||||
|
if save == 1:
|
||||||
|
plt.savefig(filename+file_format, dpi=dpi)
|
||||||
|
if show == 1:
|
||||||
|
plt.show()
|
||||||
|
plt.close('all')
|
||||||
|
|
||||||
|
# 两组横坐标数据，两组纵坐标数据画图
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def plot_two_array_with_two_horizontal_array(x1_array, x2_array, y1_array, y2_array, xlabel='x', ylabel='y', title='', fontsize=20, labelsize=20, show=1, save=0, filename='a', file_format='.jpg', dpi=300, style_1='', style_2='', y_min=None, y_max=None, linewidth_1=None, linewidth_2=None, markersize_1=None, markersize_2=None, adjust_bottom=0.2, adjust_left=0.2):
|
||||||
|
import guan
|
||||||
|
plt, fig, ax = guan.import_plt_and_start_fig_ax(adjust_bottom=adjust_bottom, adjust_left=adjust_left, labelsize=labelsize)
|
||||||
|
ax.plot(x1_array, y1_array, style_1, linewidth=linewidth_1, markersize=markersize_1)
|
||||||
|
ax.plot(x2_array, y2_array, style_2, linewidth=linewidth_2, markersize=markersize_2)
|
||||||
|
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')
|
||||||
|
if y_min!=None or y_max!=None:
|
||||||
|
if y_min==None:
|
||||||
|
y1_min=min(y1_array)
|
||||||
|
y2_min=min(y2_array)
|
||||||
|
y_min=min([y1_min, y2_min])
|
||||||
|
if y_max==None:
|
||||||
|
y1_max=max(y1_array)
|
||||||
|
y2_max=max(y2_array)
|
||||||
|
y_max=max([y1_max, y2_max])
|
||||||
|
ax.set_ylim(y_min, y_max)
|
||||||
|
if save == 1:
|
||||||
|
plt.savefig(filename+file_format, dpi=dpi)
|
||||||
|
if show == 1:
|
||||||
|
plt.show()
|
||||||
|
plt.close('all')
|
||||||
|
|
||||||
|
# 一组横坐标数据，三组纵坐标数据画图
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def plot_three_array(x_array, y1_array, y2_array, y3_array, xlabel='x', ylabel='y', title='', fontsize=20, labelsize=20, show=1, save=0, filename='a', file_format='.jpg', dpi=300, style_1='', style_2='', style_3='', y_min=None, y_max=None, linewidth_1=None, linewidth_2=None, linewidth_3=None,markersize_1=None, markersize_2=None, markersize_3=None, adjust_bottom=0.2, adjust_left=0.2):
|
||||||
|
import guan
|
||||||
|
plt, fig, ax = guan.import_plt_and_start_fig_ax(adjust_bottom=adjust_bottom, adjust_left=adjust_left, labelsize=labelsize)
|
||||||
|
ax.plot(x_array, y1_array, style_1, linewidth=linewidth_1, markersize=markersize_1)
|
||||||
|
ax.plot(x_array, y2_array, style_2, linewidth=linewidth_2, markersize=markersize_2)
|
||||||
|
ax.plot(x_array, y3_array, style_3, linewidth=linewidth_3, markersize=markersize_3)
|
||||||
|
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')
|
||||||
|
if y_min!=None or y_max!=None:
|
||||||
|
if y_min==None:
|
||||||
|
y1_min=min(y1_array)
|
||||||
|
y2_min=min(y2_array)
|
||||||
|
y3_min=min(y3_array)
|
||||||
|
y_min=min([y1_min, y2_min, y3_min])
|
||||||
|
if y_max==None:
|
||||||
|
y1_max=max(y1_array)
|
||||||
|
y2_max=max(y2_array)
|
||||||
|
y3_max=max(y3_array)
|
||||||
|
y_max=max([y1_max, y2_max, y3_max])
|
||||||
|
ax.set_ylim(y_min, y_max)
|
||||||
|
if save == 1:
|
||||||
|
plt.savefig(filename+file_format, dpi=dpi)
|
||||||
|
if show == 1:
|
||||||
|
plt.show()
|
||||||
|
plt.close('all')
|
||||||
|
|
||||||
|
# 三组横坐标数据，三组纵坐标数据画图
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def plot_three_array_with_three_horizontal_array(x1_array, x2_array, x3_array, y1_array, y2_array, y3_array, xlabel='x', ylabel='y', title='', fontsize=20, labelsize=20, show=1, save=0, filename='a', file_format='.jpg', dpi=300, style_1='', style_2='', style_3='', y_min=None, y_max=None, linewidth_1=None, linewidth_2=None, linewidth_3=None,markersize_1=None, markersize_2=None, markersize_3=None, adjust_bottom=0.2, adjust_left=0.2):
|
||||||
|
import guan
|
||||||
|
plt, fig, ax = guan.import_plt_and_start_fig_ax(adjust_bottom=adjust_bottom, adjust_left=adjust_left, labelsize=labelsize)
|
||||||
|
ax.plot(x1_array, y1_array, style_1, linewidth=linewidth_1, markersize=markersize_1)
|
||||||
|
ax.plot(x2_array, y2_array, style_2, linewidth=linewidth_2, markersize=markersize_2)
|
||||||
|
ax.plot(x3_array, y3_array, style_3, linewidth=linewidth_3, markersize=markersize_3)
|
||||||
|
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')
|
||||||
|
if y_min!=None or y_max!=None:
|
||||||
|
if y_min==None:
|
||||||
|
y1_min=min(y1_array)
|
||||||
|
y2_min=min(y2_array)
|
||||||
|
y3_min=min(y3_array)
|
||||||
|
y_min=min([y1_min, y2_min, y3_min])
|
||||||
|
if y_max==None:
|
||||||
|
y1_max=max(y1_array)
|
||||||
|
y2_max=max(y2_array)
|
||||||
|
y3_max=max(y3_array)
|
||||||
|
y_max=max([y1_max, y2_max, y3_max])
|
||||||
|
ax.set_ylim(y_min, y_max)
|
||||||
|
if save == 1:
|
||||||
|
plt.savefig(filename+file_format, dpi=dpi)
|
||||||
|
if show == 1:
|
||||||
|
plt.show()
|
||||||
|
plt.close('all')
|
||||||
|
|
||||||
|
# 画三维图
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def plot_3d_surface(x_array, y_array, matrix, xlabel='x', ylabel='y', zlabel='z', title='', fontsize=20, labelsize=15, show=1, save=0, filename='a', file_format='.jpg', dpi=300, z_min=None, z_max=None, rcount=100, ccount=100):
|
||||||
|
import numpy as np
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
from matplotlib import cm
|
||||||
|
from matplotlib.ticker import LinearLocator
|
||||||
|
matrix = np.array(matrix)
|
||||||
|
fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
|
||||||
|
plt.subplots_adjust(bottom=0.1, right=0.65)
|
||||||
|
x_array, y_array = np.meshgrid(x_array, y_array)
|
||||||
|
if len(matrix.shape) == 2:
|
||||||
|
surf = ax.plot_surface(x_array, y_array, matrix, rcount=rcount, ccount=ccount, cmap=cm.coolwarm, linewidth=0, antialiased=False)
|
||||||
|
elif len(matrix.shape) == 3:
|
||||||
|
for i0 in range(matrix.shape[2]):
|
||||||
|
surf = ax.plot_surface(x_array, y_array, matrix[:,:,i0], rcount=rcount, ccount=ccount, cmap=cm.coolwarm, linewidth=0, antialiased=False)
|
||||||
|
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.set_zlabel(zlabel, fontsize=fontsize, fontfamily='Times New Roman')
|
||||||
|
ax.zaxis.set_major_locator(LinearLocator(5))
|
||||||
|
ax.zaxis.set_major_formatter('{x:.2f}')
|
||||||
|
if z_min!=None or z_max!=None:
|
||||||
|
if z_min==None:
|
||||||
|
z_min=matrix.min()
|
||||||
|
if z_max==None:
|
||||||
|
z_max=matrix.max()
|
||||||
|
ax.set_zlim(z_min, z_max)
|
||||||
|
ax.tick_params(labelsize=labelsize)
|
||||||
|
labels = ax.get_xticklabels() + ax.get_yticklabels() + ax.get_zticklabels()
|
||||||
|
[label.set_fontname('Times New Roman') for label in labels]
|
||||||
|
cax = plt.axes([0.8, 0.1, 0.05, 0.8])
|
||||||
|
cbar = fig.colorbar(surf, 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')
|
||||||
|
|
||||||
|
# 画Contour图
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def plot_contour(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 numpy as np
|
||||||
|
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.contourf(x_array,y_array,matrix,cmap=cmap, levels=levels)
|
||||||
|
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')
|
||||||
|
|
||||||
|
# 画棋盘图/伪彩色图
|
||||||
|
@guan.statistics_decorator
|
||||||
|
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 numpy as np
|
||||||
|
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')
|
||||||
|
|
||||||
|
# 通过坐标画点和线
|
||||||
|
@guan.statistics_decorator
|
||||||
|
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 numpy as np
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
coordinate_array = np.array(coordinate_array)
|
||||||
|
print(coordinate_array.shape)
|
||||||
|
x_range = max(coordinate_array[:, 0])-min(coordinate_array[:, 0])
|
||||||
|
y_range = max(coordinate_array[:, 1])-min(coordinate_array[:, 1])
|
||||||
|
fig, ax = plt.subplots(figsize=(6*x_range/y_range,6))
|
||||||
|
plt.subplots_adjust(left=0, bottom=0, right=1, top=1)
|
||||||
|
plt.axis('off')
|
||||||
|
if draw_lines==1:
|
||||||
|
for i1 in range(coordinate_array.shape[0]):
|
||||||
|
for i2 in range(coordinate_array.shape[0]):
|
||||||
|
if np.sqrt((coordinate_array[i1, 0] - coordinate_array[i2, 0])**2+(coordinate_array[i1, 1] - coordinate_array[i2, 1])**2) < max_distance:
|
||||||
|
ax.plot([coordinate_array[i1, 0], coordinate_array[i2, 0]], [coordinate_array[i1, 1], coordinate_array[i2, 1]], line_style, linewidth=linewidth)
|
||||||
|
if draw_dots==1:
|
||||||
|
for i in range(coordinate_array.shape[0]):
|
||||||
|
ax.plot(coordinate_array[i, 0], coordinate_array[i, 1], dot_style, markersize=markersize)
|
||||||
|
if show==1:
|
||||||
|
plt.show()
|
||||||
|
if save==1:
|
||||||
|
if file_format=='.eps':
|
||||||
|
plt.savefig(filename+file_format)
|
||||||
|
else:
|
||||||
|
plt.savefig(filename+file_format, dpi=dpi)
|
||||||
|
|
||||||
|
# 合并两个图片
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def combine_two_images(image_path_array, figsize=(16,8), show=0, save=1, filename='a', file_format='.jpg', dpi=300):
|
||||||
|
import numpy as np
|
||||||
|
num = np.array(image_path_array).shape[0]
|
||||||
|
if num != 2:
|
||||||
|
print('Error: The number of images should be two!')
|
||||||
|
else:
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
import matplotlib.image as mpimg
|
||||||
|
fig = plt.figure(figsize=figsize)
|
||||||
|
plt.subplots_adjust(left=0, right=1, bottom=0, top=1, wspace=0, hspace=0)
|
||||||
|
ax1 = fig.add_subplot(121)
|
||||||
|
ax2 = fig.add_subplot(122)
|
||||||
|
image_1 = mpimg.imread(image_path_array[0])
|
||||||
|
image_2 = mpimg.imread(image_path_array[1])
|
||||||
|
ax1.imshow(image_1)
|
||||||
|
ax2.imshow(image_2)
|
||||||
|
ax1.axis('off')
|
||||||
|
ax2.axis('off')
|
||||||
|
if show == 1:
|
||||||
|
plt.show()
|
||||||
|
if save == 1:
|
||||||
|
plt.savefig(filename+file_format, dpi=dpi)
|
||||||
|
plt.close('all')
|
||||||
|
|
||||||
|
# 合并三个图片
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def combine_three_images(image_path_array, figsize=(16,5), show=0, save=1, filename='a', file_format='.jpg', dpi=300):
|
||||||
|
import numpy as np
|
||||||
|
num = np.array(image_path_array).shape[0]
|
||||||
|
if num != 3:
|
||||||
|
print('Error: The number of images should be three!')
|
||||||
|
else:
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
import matplotlib.image as mpimg
|
||||||
|
fig = plt.figure(figsize=figsize)
|
||||||
|
plt.subplots_adjust(left=0, right=1, bottom=0, top=1, wspace=0, hspace=0)
|
||||||
|
ax1 = fig.add_subplot(131)
|
||||||
|
ax2 = fig.add_subplot(132)
|
||||||
|
ax3 = fig.add_subplot(133)
|
||||||
|
image_1 = mpimg.imread(image_path_array[0])
|
||||||
|
image_2 = mpimg.imread(image_path_array[1])
|
||||||
|
image_3 = mpimg.imread(image_path_array[2])
|
||||||
|
ax1.imshow(image_1)
|
||||||
|
ax2.imshow(image_2)
|
||||||
|
ax3.imshow(image_3)
|
||||||
|
ax1.axis('off')
|
||||||
|
ax2.axis('off')
|
||||||
|
ax3.axis('off')
|
||||||
|
if show == 1:
|
||||||
|
plt.show()
|
||||||
|
if save == 1:
|
||||||
|
plt.savefig(filename+file_format, dpi=dpi)
|
||||||
|
plt.close('all')
|
||||||
|
|
||||||
|
# 合并四个图片
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def combine_four_images(image_path_array, figsize=(16,16), show=0, save=1, filename='a', file_format='.jpg', dpi=300):
|
||||||
|
import numpy as np
|
||||||
|
num = np.array(image_path_array).shape[0]
|
||||||
|
if num != 4:
|
||||||
|
print('Error: The number of images should be four!')
|
||||||
|
else:
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
import matplotlib.image as mpimg
|
||||||
|
fig = plt.figure(figsize=figsize)
|
||||||
|
plt.subplots_adjust(left=0, right=1, bottom=0, top=1, wspace=0, hspace=0)
|
||||||
|
ax1 = fig.add_subplot(221)
|
||||||
|
ax2 = fig.add_subplot(222)
|
||||||
|
ax3 = fig.add_subplot(223)
|
||||||
|
ax4 = fig.add_subplot(224)
|
||||||
|
image_1 = mpimg.imread(image_path_array[0])
|
||||||
|
image_2 = mpimg.imread(image_path_array[1])
|
||||||
|
image_3 = mpimg.imread(image_path_array[2])
|
||||||
|
image_4 = mpimg.imread(image_path_array[3])
|
||||||
|
ax1.imshow(image_1)
|
||||||
|
ax2.imshow(image_2)
|
||||||
|
ax3.imshow(image_3)
|
||||||
|
ax4.imshow(image_4)
|
||||||
|
ax1.axis('off')
|
||||||
|
ax2.axis('off')
|
||||||
|
ax3.axis('off')
|
||||||
|
ax4.axis('off')
|
||||||
|
if show == 1:
|
||||||
|
plt.show()
|
||||||
|
if save == 1:
|
||||||
|
plt.savefig(filename+file_format, dpi=dpi)
|
||||||
|
plt.close('all')
|
||||||
|
|
||||||
|
# 对某个目录中的txt文件批量读取和画图
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def batch_reading_and_plotting(directory, xlabel='x', ylabel='y'):
|
||||||
|
import re
|
||||||
|
import os
|
||||||
|
import guan
|
||||||
|
for root, dirs, files in os.walk(directory):
|
||||||
|
for file in files:
|
||||||
|
if re.search('^txt.', file[::-1]):
|
||||||
|
filename = file[:-4]
|
||||||
|
x_array, y_array = guan.read_one_dimensional_data(filename=filename)
|
||||||
|
guan.plot(x_array, y_array, xlabel=xlabel, ylabel=ylabel, title=filename, show=0, save=1, filename=filename)
|
||||||
|
|
||||||
|
# 将图片制作GIF动画
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def make_gif(image_path_array, filename='a', duration=0.1):
|
||||||
|
import imageio
|
||||||
|
images = []
|
||||||
|
for image_path in image_path_array:
|
||||||
|
im = imageio.imread(image_path)
|
||||||
|
images.append(im)
|
||||||
|
imageio.mimsave(filename+'.gif', images, 'GIF', duration=duration)
|
||||||
|
|
||||||
|
# 选取Matplotlib颜色
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def color_matplotlib():
|
||||||
|
color_array = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan']
|
||||||
|
return color_array
|
||||||
337
PyPI/src/guan/file_reading_and_writing.py
Normal file
337
PyPI/src/guan/file_reading_and_writing.py
Normal file
@ -0,0 +1,337 @@
|
|||||||
|
# Module: file_reading_and_writing
|
||||||
|
import guan
|
||||||
|
|
||||||
|
# 使用pickle将变量保存到文件（支持几乎所有对象类型）
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def dump_data(data, filename, file_format='.txt'):
|
||||||
|
import pickle
|
||||||
|
with open(filename+file_format, 'wb') as f:
|
||||||
|
pickle.dump(data, f)
|
||||||
|
|
||||||
|
# 使用pickle从文件中恢复数据到变量（支持几乎所有对象类型）
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def load_data(filename, file_format='.txt'):
|
||||||
|
import pickle
|
||||||
|
with open(filename+file_format, 'rb') as f:
|
||||||
|
data = pickle.load(f)
|
||||||
|
return data
|
||||||
|
|
||||||
|
# 使用NumPy保存数组变量到npy文件（二进制文件）
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def save_npy_data(data, filename):
|
||||||
|
import numpy as np
|
||||||
|
np.save(filename+'.npy', data)
|
||||||
|
|
||||||
|
# 使用NumPy从npy文件恢复数据到数组变量（二进制文件）
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def load_npy_data(filename):
|
||||||
|
import numpy as np
|
||||||
|
data = np.load(filename+'.npy')
|
||||||
|
return data
|
||||||
|
|
||||||
|
# 使用NumPy保存数组变量到TXT文件（文本文件）
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def save_txt_data(data, filename):
|
||||||
|
import numpy as np
|
||||||
|
np.savetxt(filename+'.txt', data)
|
||||||
|
|
||||||
|
# 使用NumPy从TXT文件恢复数据到数组变量（文本文件）
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def load_txt_data(filename):
|
||||||
|
import numpy as np
|
||||||
|
data = np.loadtxt(filename+'.txt')
|
||||||
|
return data
|
||||||
|
|
||||||
|
# 读取文件中的一维数据（一行一组x和y）
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def read_one_dimensional_data(filename='a', file_format='.txt'):
|
||||||
|
import numpy as np
|
||||||
|
f = open(filename+file_format, 'r')
|
||||||
|
text = f.read()
|
||||||
|
f.close()
|
||||||
|
row_list = np.array(text.split('\n'))
|
||||||
|
dim_column = np.array(row_list[0].split()).shape[0]
|
||||||
|
x_array = np.array([])
|
||||||
|
y_array = np.array([])
|
||||||
|
for row in row_list:
|
||||||
|
column = np.array(row.split())
|
||||||
|
if column.shape[0] != 0:
|
||||||
|
x_array = np.append(x_array, [float(column[0])], axis=0)
|
||||||
|
y_row = np.zeros(dim_column-1)
|
||||||
|
for dim0 in range(dim_column-1):
|
||||||
|
y_row[dim0] = float(column[dim0+1])
|
||||||
|
if np.array(y_array).shape[0] == 0:
|
||||||
|
y_array = [y_row]
|
||||||
|
else:
|
||||||
|
y_array = np.append(y_array, [y_row], axis=0)
|
||||||
|
return x_array, y_array
|
||||||
|
|
||||||
|
# 读取文件中的一维数据（一行一组x和y）（支持复数形式）
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def read_one_dimensional_complex_data(filename='a', file_format='.txt'):
|
||||||
|
import numpy as np
|
||||||
|
f = open(filename+file_format, 'r')
|
||||||
|
text = f.read()
|
||||||
|
f.close()
|
||||||
|
row_list = np.array(text.split('\n'))
|
||||||
|
dim_column = np.array(row_list[0].split()).shape[0]
|
||||||
|
x_array = np.array([])
|
||||||
|
y_array = np.array([])
|
||||||
|
for row in row_list:
|
||||||
|
column = np.array(row.split())
|
||||||
|
if column.shape[0] != 0:
|
||||||
|
x_array = np.append(x_array, [complex(column[0])], axis=0)
|
||||||
|
y_row = np.zeros(dim_column-1, dtype=complex)
|
||||||
|
for dim0 in range(dim_column-1):
|
||||||
|
y_row[dim0] = complex(column[dim0+1])
|
||||||
|
if np.array(y_array).shape[0] == 0:
|
||||||
|
y_array = [y_row]
|
||||||
|
else:
|
||||||
|
y_array = np.append(y_array, [y_row], axis=0)
|
||||||
|
return x_array, y_array
|
||||||
|
|
||||||
|
# 读取文件中的二维数据（第一行和第一列分别为横纵坐标）
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def read_two_dimensional_data(filename='a', file_format='.txt'):
|
||||||
|
import numpy as np
|
||||||
|
f = open(filename+file_format, 'r')
|
||||||
|
text = f.read()
|
||||||
|
f.close()
|
||||||
|
row_list = np.array(text.split('\n'))
|
||||||
|
dim_column = np.array(row_list[0].split()).shape[0]
|
||||||
|
x_array = np.array([])
|
||||||
|
y_array = np.array([])
|
||||||
|
matrix = np.array([])
|
||||||
|
for i0 in range(row_list.shape[0]):
|
||||||
|
column = np.array(row_list[i0].split())
|
||||||
|
if i0 == 0:
|
||||||
|
x_str = column[1::]
|
||||||
|
x_array = np.zeros(x_str.shape[0])
|
||||||
|
for i00 in range(x_str.shape[0]):
|
||||||
|
x_array[i00] = float(x_str[i00])
|
||||||
|
elif column.shape[0] != 0:
|
||||||
|
y_array = np.append(y_array, [float(column[0])], axis=0)
|
||||||
|
matrix_row = np.zeros(dim_column-1)
|
||||||
|
for dim0 in range(dim_column-1):
|
||||||
|
matrix_row[dim0] = float(column[dim0+1])
|
||||||
|
if np.array(matrix).shape[0] == 0:
|
||||||
|
matrix = [matrix_row]
|
||||||
|
else:
|
||||||
|
matrix = np.append(matrix, [matrix_row], axis=0)
|
||||||
|
return x_array, y_array, matrix
|
||||||
|
|
||||||
|
# 读取文件中的二维数据（第一行和第一列分别为横纵坐标）（支持复数形式）
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def read_two_dimensional_complex_data(filename='a', file_format='.txt'):
|
||||||
|
import numpy as np
|
||||||
|
f = open(filename+file_format, 'r')
|
||||||
|
text = f.read()
|
||||||
|
f.close()
|
||||||
|
row_list = np.array(text.split('\n'))
|
||||||
|
dim_column = np.array(row_list[0].split()).shape[0]
|
||||||
|
x_array = np.array([])
|
||||||
|
y_array = np.array([])
|
||||||
|
matrix = np.array([])
|
||||||
|
for i0 in range(row_list.shape[0]):
|
||||||
|
column = np.array(row_list[i0].split())
|
||||||
|
if i0 == 0:
|
||||||
|
x_str = column[1::]
|
||||||
|
x_array = np.zeros(x_str.shape[0], dtype=complex)
|
||||||
|
for i00 in range(x_str.shape[0]):
|
||||||
|
x_array[i00] = complex(x_str[i00])
|
||||||
|
elif column.shape[0] != 0:
|
||||||
|
y_array = np.append(y_array, [complex(column[0])], axis=0)
|
||||||
|
matrix_row = np.zeros(dim_column-1, dtype=complex)
|
||||||
|
for dim0 in range(dim_column-1):
|
||||||
|
matrix_row[dim0] = complex(column[dim0+1])
|
||||||
|
if np.array(matrix).shape[0] == 0:
|
||||||
|
matrix = [matrix_row]
|
||||||
|
else:
|
||||||
|
matrix = np.append(matrix, [matrix_row], axis=0)
|
||||||
|
return x_array, y_array, matrix
|
||||||
|
|
||||||
|
# 读取文件中的二维数据（不包括x和y）
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def read_two_dimensional_data_without_xy_array(filename='a', file_format='.txt'):
|
||||||
|
import numpy as np
|
||||||
|
matrix = np.loadtxt(filename+file_format)
|
||||||
|
return matrix
|
||||||
|
|
||||||
|
# 打开文件用于新增内容
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def open_file(filename='a', file_format='.txt'):
|
||||||
|
f = open(filename+file_format, 'a', encoding='UTF-8')
|
||||||
|
return f
|
||||||
|
|
||||||
|
# 在文件中写入一维数据（一行一组x和y）
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def write_one_dimensional_data(x_array, y_array, filename='a', file_format='.txt'):
|
||||||
|
import guan
|
||||||
|
with open(filename+file_format, 'w', encoding='UTF-8') as f:
|
||||||
|
guan.write_one_dimensional_data_without_opening_file(x_array, y_array, f)
|
||||||
|
|
||||||
|
# 在文件中写入一维数据（一行一组x和y）（需要输入已打开的文件）
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def write_one_dimensional_data_without_opening_file(x_array, y_array, f):
|
||||||
|
import numpy as np
|
||||||
|
x_array = np.array(x_array)
|
||||||
|
y_array = np.array(y_array)
|
||||||
|
i0 = 0
|
||||||
|
for x0 in x_array:
|
||||||
|
f.write(str(x0)+' ')
|
||||||
|
if len(y_array.shape) == 1:
|
||||||
|
f.write(str(y_array[i0])+'\n')
|
||||||
|
elif len(y_array.shape) == 2:
|
||||||
|
for j0 in range(y_array.shape[1]):
|
||||||
|
f.write(str(y_array[i0, j0])+' ')
|
||||||
|
f.write('\n')
|
||||||
|
i0 += 1
|
||||||
|
|
||||||
|
# 在文件中写入二维数据（第一行和第一列分别为横纵坐标）
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def write_two_dimensional_data(x_array, y_array, matrix, filename='a', file_format='.txt'):
|
||||||
|
import guan
|
||||||
|
with open(filename+file_format, 'w', encoding='UTF-8') as f:
|
||||||
|
guan.write_two_dimensional_data_without_opening_file(x_array, y_array, matrix, f)
|
||||||
|
|
||||||
|
# 在文件中写入二维数据（第一行和第一列分别为横纵坐标）（需要输入已打开的文件）
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def write_two_dimensional_data_without_opening_file(x_array, y_array, matrix, f):
|
||||||
|
import numpy as np
|
||||||
|
x_array = np.array(x_array)
|
||||||
|
y_array = np.array(y_array)
|
||||||
|
matrix = np.array(matrix)
|
||||||
|
f.write('0 ')
|
||||||
|
for x0 in x_array:
|
||||||
|
f.write(str(x0)+' ')
|
||||||
|
f.write('\n')
|
||||||
|
i0 = 0
|
||||||
|
for y0 in y_array:
|
||||||
|
f.write(str(y0))
|
||||||
|
j0 = 0
|
||||||
|
for x0 in x_array:
|
||||||
|
f.write(' '+str(matrix[i0, j0])+' ')
|
||||||
|
j0 += 1
|
||||||
|
f.write('\n')
|
||||||
|
i0 += 1
|
||||||
|
|
||||||
|
# 在文件中写入二维数据（不包括x和y）
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def write_two_dimensional_data_without_xy_array(matrix, filename='a', file_format='.txt'):
|
||||||
|
import guan
|
||||||
|
with open(filename+file_format, 'w', encoding='UTF-8') as f:
|
||||||
|
guan.write_two_dimensional_data_without_xy_array_and_without_opening_file(matrix, f)
|
||||||
|
|
||||||
|
# 在文件中写入二维数据（不包括x和y）（需要输入已打开的文件）
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def write_two_dimensional_data_without_xy_array_and_without_opening_file(matrix, f):
|
||||||
|
for row in matrix:
|
||||||
|
for element in row:
|
||||||
|
f.write(str(element)+' ')
|
||||||
|
f.write('\n')
|
||||||
|
|
||||||
|
# 如果不存在文件夹，则新建文件夹
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def make_directory(directory='./test'):
|
||||||
|
import os
|
||||||
|
if not os.path.exists(directory):
|
||||||
|
os.makedirs(directory)
|
||||||
|
|
||||||
|
# 如果不存在文件，则新建空文件
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def make_file(file_path='./a.txt'):
|
||||||
|
import os
|
||||||
|
if not os.path.exists(file_path):
|
||||||
|
with open(file_path, 'w') as f:
|
||||||
|
pass
|
||||||
|
|
||||||
|
# 读取文本文件内容，如果不存在，则新建空文件
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def read_text_file(file_path='./a.txt'):
|
||||||
|
import os
|
||||||
|
if not os.path.exists(file_path):
|
||||||
|
with open(file_path, 'w') as f:
|
||||||
|
pass
|
||||||
|
return ''
|
||||||
|
else:
|
||||||
|
with open(file_path, 'r') as f:
|
||||||
|
content = f.read()
|
||||||
|
return content
|
||||||
|
|
||||||
|
# 获取目录中的所有文件名
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def get_all_filenames_in_directory(directory='./', file_format=None):
|
||||||
|
import os
|
||||||
|
file_list = []
|
||||||
|
for root, dirs, files in os.walk(directory):
|
||||||
|
for i0 in range(len(files)):
|
||||||
|
if file_format == None:
|
||||||
|
file_list.append(files[i0])
|
||||||
|
else:
|
||||||
|
if file_format in files[i0]:
|
||||||
|
file_list.append(files[i0])
|
||||||
|
return file_list
|
||||||
|
|
||||||
|
# 获取目录中的所有文件名（不包括子目录）
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def get_all_filenames_in_directory_without_subdirectory(directory='./', file_format=None):
|
||||||
|
import os
|
||||||
|
file_list = []
|
||||||
|
for root, dirs, files in os.walk(directory):
|
||||||
|
for i0 in range(len(files)):
|
||||||
|
if file_format == None:
|
||||||
|
file_list.append(files[i0])
|
||||||
|
else:
|
||||||
|
if file_format in files[i0]:
|
||||||
|
file_list.append(files[i0])
|
||||||
|
break
|
||||||
|
return file_list
|
||||||
|
|
||||||
|
# 获取文件夹中某种文本类型的文件路径以及读取内容
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def read_text_files_in_directory(directory='./', file_format='.md'):
|
||||||
|
import os
|
||||||
|
file_list = []
|
||||||
|
for root, dirs, files in os.walk(directory):
|
||||||
|
for i0 in range(len(files)):
|
||||||
|
if file_format in files[i0]:
|
||||||
|
file_list.append(root+'/'+files[i0])
|
||||||
|
content_array = []
|
||||||
|
for file in file_list:
|
||||||
|
with open(file, 'r') as f:
|
||||||
|
content_array.append(f.read())
|
||||||
|
return file_list, content_array
|
||||||
|
|
||||||
|
# 在多个文本文件中查找关键词
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def find_words_in_multiple_files(words, directory='./', file_format='.md'):
|
||||||
|
import guan
|
||||||
|
file_list, content_array = guan.read_text_files_in_directory(directory=directory, file_format=file_format)
|
||||||
|
num_files = len(file_list)
|
||||||
|
file_list_with_words = []
|
||||||
|
for i0 in range(num_files):
|
||||||
|
if words in content_array[i0]:
|
||||||
|
file_list_with_words.append(file_list[i0])
|
||||||
|
return file_list_with_words
|
||||||
|
|
||||||
|
# 复制一份文件
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def copy_file(file1='./a.txt', file2='./b.txt'):
|
||||||
|
import shutil
|
||||||
|
shutil.copy(file1, file2)
|
||||||
|
|
||||||
|
# 拼接两个PDF文件
|
||||||
|
@guan.statistics_decorator
|
||||||
|
def combine_two_pdf_files(input_file_1='a.pdf', input_file_2='b.pdf', output_file='combined_file.pdf'):
|
||||||
|
import PyPDF2
|
||||||
|
output_pdf = PyPDF2.PdfWriter()
|
||||||
|
with open(input_file_1, 'rb') as file1:
|
||||||
|
pdf1 = PyPDF2.PdfReader(file1)
|
||||||
|
for page in range(len(pdf1.pages)):
|
||||||
|
output_pdf.add_page(pdf1.pages[page])
|
||||||
|
with open(input_file_2, 'rb') as file2:
|
||||||
|
pdf2 = PyPDF2.PdfReader(file2)
|
||||||
|
for page in range(len(pdf2.pages)):
|
||||||
|
output_pdf.add_page(pdf2.pages[page])
|
||||||
|
with open(output_file, 'wb') as combined_file:
|
||||||
|
output_pdf.write(combined_file)
|
||||||
@ -165,9 +165,9 @@ def batch_train_model(model, train_loader, optimizer='Adam', learning_rate=0.001
|
|||||||
loss.backward()
|
loss.backward()
|
||||||
optimizer.step()
|
optimizer.step()
|
||||||
losses.append(loss.item())
|
losses.append(loss.item())
|
||||||
if print_show == 1:
|
if print_show == 1:
|
||||||
if (epoch + 1) % 100 == 0:
|
if (epoch + 1) % 100 == 0:
|
||||||
print(epoch, loss.item())
|
print(epoch, loss.item())
|
||||||
return model, losses
|
return model, losses
|
||||||
|
|
||||||
# 保存模型参数到文件
|
# 保存模型参数到文件
|
||||||
|
|||||||
@ -19,76 +19,6 @@ def get_time(colon=True):
|
|||||||
datetime_time = datetime_time.replace(':', '')
|
datetime_time = datetime_time.replace(':', '')
|
||||||
return datetime_time
|
return datetime_time
|
||||||
|
|
||||||
# 判断某个字符是中文还是英文或其他
|
|
||||||
@guan.statistics_decorator
|
|
||||||
def check_Chinese_or_English(a):
|
|
||||||
if '\u4e00' <= a <= '\u9fff' :
|
|
||||||
word_type = 'Chinese'
|
|
||||||
elif '\x00' <= a <= '\xff':
|
|
||||||
word_type = 'English'
|
|
||||||
else:
|
|
||||||
word_type = 'Others'
|
|
||||||
return word_type
|
|
||||||
|
|
||||||
# 统计中英文文本的字数，默认不包括空格
|
|
||||||
@guan.statistics_decorator
|
|
||||||
def count_words(text, include_space=0, show_words=0):
|
|
||||||
import jieba
|
|
||||||
import guan
|
|
||||||
words = jieba.lcut(text)
|
|
||||||
new_words = []
|
|
||||||
if include_space == 0:
|
|
||||||
for word in words:
|
|
||||||
if word != ' ':
|
|
||||||
new_words.append(word)
|
|
||||||
else:
|
|
||||||
new_words = words
|
|
||||||
num_words = 0
|
|
||||||
new_words_2 = []
|
|
||||||
for word in new_words:
|
|
||||||
word_type = guan.check_Chinese_or_English(word[0])
|
|
||||||
if word_type == 'Chinese':
|
|
||||||
num_words += len(word)
|
|
||||||
for one_word in word:
|
|
||||||
new_words_2.append(one_word)
|
|
||||||
elif word_type == 'English' or 'Others':
|
|
||||||
num_words += 1
|
|
||||||
new_words_2.append(word)
|
|
||||||
if show_words == 1:
|
|
||||||
print(new_words_2)
|
|
||||||
return num_words
|
|
||||||
|
|
||||||
# 将RGB转成HEX
|
|
||||||
@guan.statistics_decorator
|
|
||||||
def rgb_to_hex(rgb, pound=1):
|
|
||||||
if pound==0:
|
|
||||||
return '%02x%02x%02x' % rgb
|
|
||||||
else:
|
|
||||||
return '#%02x%02x%02x' % rgb
|
|
||||||
|
|
||||||
# 将HEX转成RGB
|
|
||||||
@guan.statistics_decorator
|
|
||||||
def hex_to_rgb(hex):
|
|
||||||
hex = hex.lstrip('#')
|
|
||||||
length = len(hex)
|
|
||||||
return tuple(int(hex[i:i+length//3], 16) for i in range(0, length, length//3))
|
|
||||||
|
|
||||||
# 使用MD5进行散列加密
|
|
||||||
@guan.statistics_decorator
|
|
||||||
def encryption_MD5(password, salt=''):
|
|
||||||
import hashlib
|
|
||||||
password = salt+password
|
|
||||||
hashed_password = hashlib.md5(password.encode()).hexdigest()
|
|
||||||
return hashed_password
|
|
||||||
|
|
||||||
# 使用SHA-256进行散列加密
|
|
||||||
@guan.statistics_decorator
|
|
||||||
def encryption_SHA_256(password, salt=''):
|
|
||||||
import hashlib
|
|
||||||
password = salt+password
|
|
||||||
hashed_password = hashlib.sha256(password.encode()).hexdigest()
|
|
||||||
return hashed_password
|
|
||||||
|
|
||||||
# 自动先后运行程序
|
# 自动先后运行程序
|
||||||
@guan.statistics_decorator
|
@guan.statistics_decorator
|
||||||
def run_programs_sequentially(program_files=['./a.py', './b.py'], execute='python ', show_time=0):
|
def run_programs_sequentially(program_files=['./a.py', './b.py'], execute='python ', show_time=0):
|
||||||
@ -109,12 +39,6 @@ def run_programs_sequentially(program_files=['./a.py', './b.py'], execute='pytho
|
|||||||
end = time.time()
|
end = time.time()
|
||||||
print('Total running time = '+str((end-start)/60)+' min')
|
print('Total running time = '+str((end-start)/60)+' min')
|
||||||
|
|
||||||
# 复制一份文件
|
|
||||||
@guan.statistics_decorator
|
|
||||||
def copy_file(file1='./a.txt', file2='./b.txt'):
|
|
||||||
import shutil
|
|
||||||
shutil.copy(file1, file2)
|
|
||||||
|
|
||||||
# 获取CPU使用率
|
# 获取CPU使用率
|
||||||
@guan.statistics_decorator
|
@guan.statistics_decorator
|
||||||
def get_cpu_usage(interval=1):
|
def get_cpu_usage(interval=1):
|
||||||
@ -197,13 +121,6 @@ def count_number_of_import_statements(filename, file_format='.py', num=1000):
|
|||||||
import_statement_counter = Counter(import_array).most_common(num)
|
import_statement_counter = Counter(import_array).most_common(num)
|
||||||
return import_statement_counter
|
return import_statement_counter
|
||||||
|
|
||||||
# 根据一定的字符长度来分割文本
|
|
||||||
@guan.statistics_decorator
|
|
||||||
def split_text(text, wrap_width=3000):
|
|
||||||
import textwrap
|
|
||||||
split_text_list = textwrap.wrap(text, wrap_width)
|
|
||||||
return split_text_list
|
|
||||||
|
|
||||||
# 获取本月的所有日期
|
# 获取本月的所有日期
|
||||||
@guan.statistics_decorator
|
@guan.statistics_decorator
|
||||||
def get_days_of_the_current_month(str_or_datetime='str'):
|
def get_days_of_the_current_month(str_or_datetime='str'):
|
||||||
|
|||||||
@ -1,6 +1,6 @@
|
|||||||
## Guan package
|
## Guan package
|
||||||
|
|
||||||
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, such as machine learning, figure plotting and file reading/writing.
|
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, such as machine learning, figure plotting, file reading and writing, and data processing.
|
||||||
|
|
||||||
The primary location of this package is on https://py.guanjihuan.com.
|
The primary location of this package is on https://py.guanjihuan.com.
|
||||||
|
|
||||||
@ -14,6 +14,7 @@ import guan
|
|||||||
|
|
||||||
## Summary of API Reference
|
## Summary of API Reference
|
||||||
|
|
||||||
|
+ decorator
|
||||||
+ basic functions
|
+ basic functions
|
||||||
+ Fourier transform
|
+ Fourier transform
|
||||||
+ Hamiltonian of examples
|
+ Hamiltonian of examples
|
||||||
@ -23,6 +24,8 @@ import guan
|
|||||||
+ quantum transport
|
+ quantum transport
|
||||||
+ topological invariant
|
+ topological invariant
|
||||||
+ machine learning
|
+ machine learning
|
||||||
|
+ file reading and writing
|
||||||
|
+ figure plotting
|
||||||
+ data processing
|
+ data processing
|
||||||
+ others
|
+ others
|
||||||
|
|
||||||
|
|||||||
Loading…
x
Reference in New Issue
Block a user