目前本篇内容已不常更新，更多内容可前往开源项目Guan：https://py.guanjihuan.com。
本篇主要参考之前几篇博文：
这里把其中的一些内容整合为一个Python文件，用于自己平时的数据处理。代码如下：
"""
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The newest version of this code is on the web page: https://www.guanjihuan.com/archives/8734
函数调用目录：
1. x, y = read_one_dimensional_data(filename='a')
2. x, y, matrix = read_two_dimensional_data(filename='a')
3. write_one_dimensional_data(x, y, filename='a')
4. write_two_dimensional_data(x, y, matrix, filename='a')
5. plot(x, y, xlabel='x', ylabel='y', title='', filename='a')
6. plot_3d_surface(x, y, matrix, xlabel='x', ylabel='y', zlabel='z', title='', filename='a')
7. plot_contour(x, y, matrix, xlabel='x', ylabel='y', title='', filename='a')
8. plot_2d_scatter(x, y, value, xlabel='x', ylabel='y', title='', filename='a')
9. plot_3d_surface(x, y, z, value, xlabel='x', ylabel='y', zlabel='z', title='', filename='a')
10. creat_animation(image_names, duration_time=0.5, filename='a')
11. eigenvalue_array = calculate_eigenvalue_with_one_paramete(x, matrix)
12. eigenvalue_array = calculate_eigenvalue_with_two_parameters(x, y, matrix)
函数对应的功能：
1. 读取filename.txt文件中的一维数据y(x)
2. 读取filename.txt文件中的二维数据matrix(x,y)
3. 把一维数据y(x)写入filename.txt文件
4. 把二维数据matrix(x,y)写入filename.txt文件
5. 画y(x)图，并保存到filename.jpg文件。具体画图格式可在函数中修改！
6. 画3d_surface图，并保存到filename.jpg文件。具体画图格式可在函数中修改！
7. 画contour图，并保存到filename.jpg文件。具体画图格式可在函数中修改！
8. 画2d_scatter图，并保存到filename.jpg文件。具体画图格式可在函数中修改！
9. 画3d_scatter图，并保存到filename.jpg文件。具体画图格式可在函数中修改！
10. 制作动画
11. 在参数x下，计算matrix函数的本征值eigenvalue_array[:, index]
12. 在参数(x,y)下，计算matrix函数的本征值eigenvalue_array[:, :, index]
"""
import numpy as np
# import os
# os.chdir('D:/data')
def main():
pass # 读取数据 + 数据处理 + 保存新数据
# 1. 读取filename.txt文件中的一维数据y(x)
def read_one_dimensional_data(filename='a'):
f = open(filename+'.txt', 'r')
text = f.read()
f.close()
row_list = np.array(text.split('\n'))
dim_column = np.array(row_list[0].split()).shape[0]
x = np.array([])
y = np.array([])
for row in row_list:
column = np.array(row.split())
if column.shape[0] != 0:
x = np.append(x, [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).shape[0] == 0:
y = [y_row]
else:
y = np.append(y, [y_row], axis=0)
return x, y
# 2. 读取filename.txt文件中的二维数据matrix(x,y)
def read_two_dimensional_data(filename='a'):
f = open(filename+'.txt', 'r')
text = f.read()
f.close()
row_list = np.array(text.split('\n'))
dim_column = np.array(row_list[0].split()).shape[0]
x = np.array([])
y = 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 = np.zeros(x_str.shape[0])
for i00 in range(x_str.shape[0]):
x[i00] = float(x_str[i00])
elif column.shape[0] != 0:
y = np.append(y, [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, y, matrix
# 3. 把一维数据y(x)写入filename.txt文件
def write_one_dimensional_data(x, y, filename='a'):
with open(filename+'.txt', 'w') as f:
i0 = 0
for x0 in x:
f.write(str(x0)+' ')
if len(y.shape) == 1:
f.write(str(y[i0])+'\n')
elif len(y.shape) == 2:
for j0 in range(y.shape[1]):
f.write(str(y[i0, j0])+' ')
f.write('\n')
i0 += 1
# 4. 把二维数据matrix(x,y)写入filename.txt文件
def write_two_dimensional_data(x, y, matrix, filename='a'):
with open(filename+'.txt', 'w') as f:
f.write('0 ')
for x0 in x:
f.write(str(x0)+' ')
f.write('\n')
i0 = 0
for y0 in y:
f.write(str(y0))
j0 = 0
for x0 in x:
f.write(' '+str(matrix[i0, j0])+' ')
j0 += 1
f.write('\n')
i0 += 1
# 5. 画y(x)图，并保存到filename.jpg文件。具体画图格式可在函数中修改。
def plot(x, y, xlabel='x', ylabel='y', title='', filename='a', show=1, save=0):
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
plt.subplots_adjust(bottom=0.20, left=0.18)
ax.plot(x, y, '-o')
ax.grid()
ax.set_title(title, fontsize=20, fontfamily='Times New Roman')
ax.set_xlabel(xlabel, fontsize=20, fontfamily='Times New Roman')
ax.set_ylabel(ylabel, fontsize=20, fontfamily='Times New Roman')
ax.tick_params(labelsize=20)
labels = ax.get_xticklabels() + ax.get_yticklabels()
[label.set_fontname('Times New Roman') for label in labels]
if save == 1:
plt.savefig(filename+'.jpg', dpi=300)
if show == 1:
plt.show()
plt.close('all')
# 6. 画3d_surface图，并保存到filename.jpg文件。具体画图格式可在函数中修改。
def plot_3d_surface(x, y, matrix, xlabel='x', ylabel='y', zlabel='z', title='', filename='a', show=1, save=0):
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.ticker import LinearLocator
fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
plt.subplots_adjust(bottom=0.1, right=0.65)
x, y = np.meshgrid(x, y)
if len(matrix.shape) == 2:
surf = ax.plot_surface(x, y, matrix, cmap=cm.coolwarm, linewidth=0, antialiased=False)
elif len(matrix.shape) == 3:
for i0 in range(matrix.shape[2]):
surf = ax.plot_surface(x, y, matrix[:,:,i0], cmap=cm.coolwarm, linewidth=0, antialiased=False)
ax.set_title(title, fontsize=20, fontfamily='Times New Roman')
ax.set_xlabel(xlabel, fontsize=20, fontfamily='Times New Roman')
ax.set_ylabel(ylabel, fontsize=20, fontfamily='Times New Roman')
ax.set_zlabel(zlabel, fontsize=20, fontfamily='Times New Roman')
ax.zaxis.set_major_locator(LinearLocator(5))
ax.zaxis.set_major_formatter('{x:.2f}')
ax.tick_params(labelsize=15)
labels = ax.get_xticklabels() + ax.get_yticklabels() + ax.get_zticklabels()
[label.set_fontname('Times New Roman') for label in labels]
cax = plt.axes([0.80, 0.15, 0.05, 0.75])
cbar = fig.colorbar(surf, cax=cax)
cbar.ax.tick_params(labelsize=15)
for l in cbar.ax.yaxis.get_ticklabels():
l.set_family('Times New Roman')
if save == 1:
plt.savefig(filename+'.jpg', dpi=300)
if show == 1:
plt.show()
plt.close('all')
# 7. 画plot_contour图，并保存到filename.jpg文件。具体画图格式可在函数中修改。
def plot_contour(x, y, matrix, xlabel='x', ylabel='y', title='', filename='a', show=1, save=0):
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.ticker import LinearLocator
fig, ax = plt.subplots()
plt.subplots_adjust(bottom=0.2, right=0.75, left = 0.16)
x, y = np.meshgrid(x, y)
contour = ax.contourf(x,y,matrix,cmap='jet')
ax.set_title(title, fontsize=20, fontfamily='Times New Roman')
ax.set_xlabel(xlabel, fontsize=20, fontfamily='Times New Roman')
ax.set_ylabel(ylabel, fontsize=20, fontfamily='Times New Roman')
ax.tick_params(labelsize=15)
labels = ax.get_xticklabels() + ax.get_yticklabels()
[label.set_fontname('Times New Roman') for label in labels]
cax = plt.axes([0.78, 0.17, 0.08, 0.71])
cbar = fig.colorbar(contour, cax=cax)
cbar.ax.tick_params(labelsize=15)
for l in cbar.ax.yaxis.get_ticklabels():
l.set_family('Times New Roman')
if save == 1:
plt.savefig(filename+'.jpg', dpi=300)
if show == 1:
plt.show()
plt.close('all')
# 8. 画2d_scatter图，并保存到filename.jpg文件。具体画图格式可在函数中修改！
def plot_2d_scatter(x, y, value, xlabel='x', ylabel='y', title='', filename='a', show=1, save=0):
import matplotlib.pyplot as plt
from matplotlib.axes._axes import _log as matplotlib_axes_logger
matplotlib_axes_logger.setLevel('ERROR')
fig = plt.figure()
ax = fig.add_subplot(111)
plt.subplots_adjust(bottom=0.2, right=0.8, left=0.2)
for i in range(np.array(x).shape[0]):
ax.scatter(x[i], y[i], marker='o', s=100*value[i], c=(1,0,0))
ax.set_title(title, fontsize=20, fontfamily='Times New Roman')
ax.set_xlabel(xlabel, fontsize=20, fontfamily='Times New Roman')
ax.set_ylabel(ylabel, fontsize=20, fontfamily='Times New Roman')
ax.tick_params(labelsize=15)
labels = ax.get_xticklabels() + ax.get_yticklabels()
[label.set_fontname('Times New Roman') for label in labels]
if save == 1:
plt.savefig(filename+'.jpg', dpi=300)
if show == 1:
plt.show()
plt.close('all')
# 9. 画3d_scatter图，并保存到filename.jpg文件。具体画图格式可在函数中修改！
def plot_3d_scatter(x, y, z, value, xlabel='x', ylabel='y', zlabel='z', title='', filename='a', show=1, save=0):
import matplotlib.pyplot as plt
from matplotlib.ticker import LinearLocator
from matplotlib.axes._axes import _log as matplotlib_axes_logger
matplotlib_axes_logger.setLevel('ERROR')
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
plt.subplots_adjust(bottom=0.1, right=0.8)
for i in range(np.array(x).shape[0]):
ax.scatter(x[i], y[i], z[i], marker='o', s=int(100*value[i]), c=(1,0,0))
ax.set_title(title, fontsize=20, fontfamily='Times New Roman')
ax.set_xlabel(xlabel, fontsize=20, fontfamily='Times New Roman')
ax.set_ylabel(ylabel, fontsize=20, fontfamily='Times New Roman')
ax.set_zlabel(zlabel, fontsize=20, fontfamily='Times New Roman')
ax.tick_params(labelsize=15)
labels = ax.get_xticklabels() + ax.get_yticklabels() + ax.get_zticklabels()
[label.set_fontname('Times New Roman') for label in labels]
if save == 1:
plt.savefig(filename+'.jpg', dpi=300)
if show == 1:
plt.show()
plt.close('all')
# 10. 制作动画
def creat_animation(image_names, duration_time=0.5, filename='a'):
import imageio
images = []
for name in image_names:
image = name+'.jpg'
im = imageio.imread(image)
images.append(im)
imageio.mimsave(filename+'.gif', images, 'GIF', duration=duration_time) # durantion是延迟时间
# 11. 在参数x下，计算matrix函数的本征值eigenvalue_array[:, index]
def calculate_eigenvalue_with_one_parameter(x, matrix):
dim_x = np.array(x).shape[0]
i0 = 0
if np.array(matrix(0)).shape==():
eigenvalue_array = np.zeros((dim_x, 1))
for x0 in x:
matrix0 = matrix(x0)
eigenvalue_array[i0, 0] = np.real(matrix0)
i0 += 1
else:
dim = np.array(matrix(0)).shape[0]
eigenvalue_array = np.zeros((dim_x, dim))
for x0 in x:
matrix0 = matrix(x0)
eigenvalue, eigenvector = np.linalg.eig(matrix0)
eigenvalue_array[i0, :] = np.sort(np.real(eigenvalue[:]))
i0 += 1
return eigenvalue_array
# 12. 在参数(x,y)下，计算matrix函数的本征值eigenvalue_array[:, :, index]
def calculate_eigenvalue_with_two_parameters(x, y, matrix):
dim_x = np.array(x).shape[0]
dim_y = np.array(y).shape[0]
if np.array(matrix(0,0)).shape==():
eigenvalue_array = np.zeros((dim_y, dim_x, 1))
i0 = 0
for y0 in y:
j0 = 0
for x0 in x:
matrix0 = matrix(x0, y0)
eigenvalue_array[i0, j0, 0] = np.real(matrix0)
j0 += 1
i0 += 1
else:
dim = np.array(matrix(0, 0)).shape[0]
eigenvalue_array = np.zeros((dim_y, dim_x, dim))
i0 = 0
for y0 in y:
j0 = 0
for x0 in x:
matrix0 = matrix(x0, y0)
eigenvalue, eigenvector = np.linalg.eig(matrix0)
eigenvalue_array[i0, j0, :] = np.sort(np.real(eigenvalue[:]))
j0 += 1
i0 += 1
return eigenvalue_array
if __name__ == "__main__":
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