update
This commit is contained in:
guanjihuan
parent
6e281b9500
commit
e25c08532d
@ -1,11 +1,11 @@
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import imageio
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import numpy as np
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import os
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os.chdir('D:/data') # 设置文件读取和保存位置
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images = []
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for i in range(1000):
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image = str(i)+'.jpg'
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im = imageio.imread(image)
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images.append(im)
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import imageio
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import numpy as np
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import os
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os.chdir('D:/data') # 设置文件读取和保存位置
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images = []
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for i in range(1000):
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image = str(i)+'.jpg'
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im = imageio.imread(image)
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images.append(im)
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imageio.mimsave("a.gif", images, 'GIF', duration=0.1) # durantion是延迟时间
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@ -1,30 +1,30 @@
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import numpy as np
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from math import *
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# import os
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# os.chdir('D:/data') # 设置路径
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def main():
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k1 = np.arange(-pi, pi, 0.05)
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k2 = np.arange(-pi, pi, 0.05)
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value = np.ones((k2.shape[0], k1.shape[0]))
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plot_matrix(k1, k2, value)
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def plot_matrix(k1, k2, matrix):
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import matplotlib.pyplot as plt
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from mpl_toolkits.mplot3d import Axes3D
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from matplotlib import cm
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from matplotlib.ticker import LinearLocator, FormatStrFormatter
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fig = plt.figure()
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ax = fig.gca(projection='3d')
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k1, k2 = np.meshgrid(k1, k2)
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ax.plot_surface(k1, k2, matrix, cmap=cm.coolwarm, linewidth=0, antialiased=False)
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plt.xlabel('k1')
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plt.ylabel('k2')
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ax.set_zlabel('Z')
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plt.show()
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if __name__ == '__main__':
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import numpy as np
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from math import *
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# import os
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# os.chdir('D:/data') # 设置路径
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def main():
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k1 = np.arange(-pi, pi, 0.05)
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k2 = np.arange(-pi, pi, 0.05)
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value = np.ones((k2.shape[0], k1.shape[0]))
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plot_matrix(k1, k2, value)
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def plot_matrix(k1, k2, matrix):
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import matplotlib.pyplot as plt
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from mpl_toolkits.mplot3d import Axes3D
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from matplotlib import cm
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from matplotlib.ticker import LinearLocator, FormatStrFormatter
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fig = plt.figure()
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ax = fig.gca(projection='3d')
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k1, k2 = np.meshgrid(k1, k2)
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ax.plot_surface(k1, k2, matrix, cmap=cm.coolwarm, linewidth=0, antialiased=False)
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plt.xlabel('k1')
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plt.ylabel('k2')
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ax.set_zlabel('Z')
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plt.show()
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if __name__ == '__main__':
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main()
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@ -1,31 +1,31 @@
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import numpy as np
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from math import *
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# import os
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# os.chdir('D:/data') # 设置路径
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def main():
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k1 = np.arange(-pi, pi, 0.05)
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k2 = np.arange(-pi, pi, 0.05)
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value = np.ones((k2.shape[0], k1.shape[0]))
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write_matrix(k1, k2, value)
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def write_matrix(k1, k2, matrix):
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with open('a.txt', 'w') as f:
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# np.set_printoptions(suppress=True) # 取消输出科学记数法
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f.write('0 ')
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for k10 in k1:
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f.write(str(k10)+' ')
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f.write('\n')
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i0 = 0
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for k20 in k2:
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f.write(str(k20))
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for j0 in range(k1.shape[0]):
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f.write(' '+str(matrix[i0, j0])+' ')
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f.write('\n')
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i0 += 1
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if __name__ == '__main__':
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import numpy as np
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from math import *
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# import os
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# os.chdir('D:/data') # 设置路径
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def main():
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k1 = np.arange(-pi, pi, 0.05)
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k2 = np.arange(-pi, pi, 0.05)
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value = np.ones((k2.shape[0], k1.shape[0]))
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write_matrix(k1, k2, value)
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def write_matrix(k1, k2, matrix):
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with open('a.txt', 'w') as f:
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# np.set_printoptions(suppress=True) # 取消输出科学记数法
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f.write('0 ')
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for k10 in k1:
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f.write(str(k10)+' ')
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f.write('\n')
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i0 = 0
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for k20 in k2:
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f.write(str(k20))
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for j0 in range(k1.shape[0]):
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f.write(' '+str(matrix[i0, j0])+' ')
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f.write('\n')
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i0 += 1
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if __name__ == '__main__':
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main()
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@ -1,35 +1,35 @@
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import numpy as np
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from math import *
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# import os
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# os.chdir('D:/data') # 设置路径
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def hamiltonian(k):
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pass
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def main():
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k = np.arange(-pi, pi, 0.05)
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plot_bands_one_dimension(k, hamiltonian)
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def plot_bands_one_dimension(k, hamiltonian):
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import matplotlib.pyplot as plt
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dim = hamiltonian(0).shape[0]
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dim_k = k.shape[0]
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eigenvalue_k = np.zeros((dim_k, dim))
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i0 = 0
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for k0 in k:
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matrix0 = hamiltonian(k0)
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eigenvalue, eigenvector = np.linalg.eig(matrix0)
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eigenvalue_k[i0, :] = np.sort(np.real(eigenvalue[:]))
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i0 += 1
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for dim0 in range(dim):
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plt.plot(k, eigenvalue_k[:, dim0], '-k')
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plt.xlabel('k')
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plt.ylabel('E')
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plt.show()
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if __name__ == '__main__':
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import numpy as np
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from math import *
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# import os
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# os.chdir('D:/data') # 设置路径
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def hamiltonian(k):
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pass
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def main():
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k = np.arange(-pi, pi, 0.05)
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plot_bands_one_dimension(k, hamiltonian)
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def plot_bands_one_dimension(k, hamiltonian):
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import matplotlib.pyplot as plt
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dim = hamiltonian(0).shape[0]
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dim_k = k.shape[0]
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eigenvalue_k = np.zeros((dim_k, dim))
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i0 = 0
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for k0 in k:
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matrix0 = hamiltonian(k0)
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eigenvalue, eigenvector = np.linalg.eig(matrix0)
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eigenvalue_k[i0, :] = np.sort(np.real(eigenvalue[:]))
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i0 += 1
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for dim0 in range(dim):
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plt.plot(k, eigenvalue_k[:, dim0], '-k')
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plt.xlabel('k')
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plt.ylabel('E')
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plt.show()
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if __name__ == '__main__':
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main()
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@ -1,47 +1,47 @@
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import numpy as np
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from math import *
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# import os
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# os.chdir('D:/data') # 设置路径
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def hamiltonian(k1, k2):
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pass
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def main():
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k1 = np.arange(-pi, pi, 0.05)
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k2 = np.arange(-pi, pi, 0.05)
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plot_bands_two_dimension(k1, k2, hamiltonian)
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def plot_bands_two_dimension(k1, k2, hamiltonian):
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import matplotlib.pyplot as plt
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from mpl_toolkits.mplot3d import Axes3D
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from matplotlib import cm
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from matplotlib.ticker import LinearLocator, FormatStrFormatter
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dim = hamiltonian(0, 0).shape[0]
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dim1 = k1.shape[0]
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dim2 = k2.shape[0]
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eigenvalue_k = np.zeros((dim2, dim1, dim))
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i0 = 0
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for k20 in k2:
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j0 = 0
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for k10 in k1:
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matrix0 = hamiltonian(k10, k20)
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eigenvalue, eigenvector = np.linalg.eig(matrix0)
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eigenvalue_k[i0, j0, :] = np.sort(np.real(eigenvalue[:]))
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j0 += 1
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i0 += 1
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fig = plt.figure()
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ax = fig.gca(projection='3d')
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k1, k2 = np.meshgrid(k1, k2)
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for dim0 in range(dim):
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ax.plot_surface(k1, k2, eigenvalue_k[:, :, dim0], cmap=cm.coolwarm, linewidth=0, antialiased=False)
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plt.xlabel('k1')
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plt.ylabel('k2')
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ax.set_zlabel('E')
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plt.show()
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if __name__ == '__main__':
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import numpy as np
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from math import *
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# import os
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# os.chdir('D:/data') # 设置路径
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def hamiltonian(k1, k2):
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pass
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def main():
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k1 = np.arange(-pi, pi, 0.05)
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k2 = np.arange(-pi, pi, 0.05)
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plot_bands_two_dimension(k1, k2, hamiltonian)
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def plot_bands_two_dimension(k1, k2, hamiltonian):
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import matplotlib.pyplot as plt
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from mpl_toolkits.mplot3d import Axes3D
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from matplotlib import cm
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from matplotlib.ticker import LinearLocator, FormatStrFormatter
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dim = hamiltonian(0, 0).shape[0]
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dim1 = k1.shape[0]
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dim2 = k2.shape[0]
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eigenvalue_k = np.zeros((dim2, dim1, dim))
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i0 = 0
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for k20 in k2:
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j0 = 0
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for k10 in k1:
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matrix0 = hamiltonian(k10, k20)
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eigenvalue, eigenvector = np.linalg.eig(matrix0)
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eigenvalue_k[i0, j0, :] = np.sort(np.real(eigenvalue[:]))
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j0 += 1
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i0 += 1
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fig = plt.figure()
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ax = fig.gca(projection='3d')
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k1, k2 = np.meshgrid(k1, k2)
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for dim0 in range(dim):
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ax.plot_surface(k1, k2, eigenvalue_k[:, :, dim0], cmap=cm.coolwarm, linewidth=0, antialiased=False)
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plt.xlabel('k1')
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plt.ylabel('k2')
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ax.set_zlabel('E')
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plt.show()
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if __name__ == '__main__':
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main()
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60
academic_codes/2020.08.29_several_python_functions/get_eigenvalues_plot_and_write/get_eigenvalues_and_write_1D_data_into_txt_file.py → language_learning/2020.08.29_several_python_functions/get_eigenvalues_plot_and_write/get_eigenvalues_and_write_1D_data_into_txt_file.py
Executable file → Normal file
60
academic_codes/2020.08.29_several_python_functions/get_eigenvalues_plot_and_write/get_eigenvalues_and_write_1D_data_into_txt_file.py → language_learning/2020.08.29_several_python_functions/get_eigenvalues_plot_and_write/get_eigenvalues_and_write_1D_data_into_txt_file.py
Executable file → Normal file
@ -1,31 +1,31 @@
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import numpy as np
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from math import *
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# import os
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# os.chdir('D:/data') # 设置路径
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def hamiltonian(k):
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pass
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def main():
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k = np.arange(-pi, pi, 0.05)
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write_bands_one_dimension(k, hamiltonian)
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def write_bands_one_dimension(k, hamiltonian):
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dim = hamiltonian(0).shape[0]
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f = open('a.txt','w')
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for k0 in k:
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f.write(str(k0)+' ')
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matrix0 = hamiltonian(k0)
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eigenvalue, eigenvector = np.linalg.eig(matrix0)
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eigenvalue = np.sort(np.real(eigenvalue))
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for dim0 in range(dim):
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f.write(str(eigenvalue[dim0])+' ')
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f.write('\n')
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f.close()
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if __name__ == '__main__':
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import numpy as np
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from math import *
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# import os
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# os.chdir('D:/data') # 设置路径
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def hamiltonian(k):
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pass
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def main():
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k = np.arange(-pi, pi, 0.05)
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write_bands_one_dimension(k, hamiltonian)
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def write_bands_one_dimension(k, hamiltonian):
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dim = hamiltonian(0).shape[0]
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f = open('a.txt','w')
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for k0 in k:
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f.write(str(k0)+' ')
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matrix0 = hamiltonian(k0)
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eigenvalue, eigenvector = np.linalg.eig(matrix0)
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eigenvalue = np.sort(np.real(eigenvalue))
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for dim0 in range(dim):
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f.write(str(eigenvalue[dim0])+' ')
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f.write('\n')
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f.close()
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if __name__ == '__main__':
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main()
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82
academic_codes/2020.08.29_several_python_functions/get_eigenvalues_plot_and_write/get_eigenvalues_and_write_2D_data_into_txt_file.py → language_learning/2020.08.29_several_python_functions/get_eigenvalues_plot_and_write/get_eigenvalues_and_write_2D_data_into_txt_file.py
Executable file → Normal file
82
academic_codes/2020.08.29_several_python_functions/get_eigenvalues_plot_and_write/get_eigenvalues_and_write_2D_data_into_txt_file.py → language_learning/2020.08.29_several_python_functions/get_eigenvalues_plot_and_write/get_eigenvalues_and_write_2D_data_into_txt_file.py
Executable file → Normal file
@ -1,42 +1,42 @@
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import numpy as np
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from math import *
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# import os
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# os.chdir('D:/data') # 设置路径
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def hamiltonian(k1, k2):
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pass
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def main():
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k1 = np.arange(-pi, pi, 0.05)
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k2 = np.arange(-pi, pi, 0.05)
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write_bands_two_dimension(k1, k2, hamiltonian)
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def write_bands_two_dimension(k1, k2, hamiltonian):
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f1 = open('a1.txt', 'w')
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f2 = open('a2.txt', 'w')
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f1.write('0 ')
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f2.write('0 ')
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for k10 in k1:
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f1.write(str(k10)+' ')
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f2.write(str(k10)+' ')
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f1.write('\n')
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f2.write('\n')
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for k20 in k2:
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f1.write(str(k20)+' ')
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f2.write(str(k20)+' ')
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for k10 in k1:
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matrix0 = hamiltonian(k10, k20)
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eigenvalue, eigenvector = np.linalg.eig(matrix0)
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eigenvalue = np.sort(np.real(eigenvalue))
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f1.write(str(eigenvalue[0])+' ')
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f2.write(str(eigenvalue[1])+' ')
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f1.write('\n')
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f2.write('\n')
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f1.close()
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f2.close()
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if __name__ == '__main__':
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import numpy as np
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from math import *
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# import os
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# os.chdir('D:/data') # 设置路径
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def hamiltonian(k1, k2):
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pass
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def main():
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k1 = np.arange(-pi, pi, 0.05)
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k2 = np.arange(-pi, pi, 0.05)
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write_bands_two_dimension(k1, k2, hamiltonian)
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def write_bands_two_dimension(k1, k2, hamiltonian):
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f1 = open('a1.txt', 'w')
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f2 = open('a2.txt', 'w')
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f1.write('0 ')
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f2.write('0 ')
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for k10 in k1:
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f1.write(str(k10)+' ')
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f2.write(str(k10)+' ')
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f1.write('\n')
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f2.write('\n')
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for k20 in k2:
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f1.write(str(k20)+' ')
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f2.write(str(k20)+' ')
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for k10 in k1:
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matrix0 = hamiltonian(k10, k20)
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eigenvalue, eigenvector = np.linalg.eig(matrix0)
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eigenvalue = np.sort(np.real(eigenvalue))
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f1.write(str(eigenvalue[0])+' ')
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f2.write(str(eigenvalue[1])+' ')
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f1.write('\n')
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f2.write('\n')
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f1.close()
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f2.close()
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if __name__ == '__main__':
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main()
|
||||
@ -1,7 +1,7 @@
|
||||
import numpy as np # 导入numpy库，用来存储和处理大型矩阵，比python自带的嵌套列表更高效。numpy库还包含了许多数学函数库。python+numpy等同于matlab。
|
||||
|
||||
def main(): # 主函数的内容放在这里。
|
||||
pass
|
||||
|
||||
if __name__ == '__main__': # 如果是当前文件直接运行，执行main()函数中的内容；如果是import当前文件，则不执行。同时将main()语句放在最后运行，可以避免书写的函数出现未定义的情况。
|
||||
import numpy as np # 导入numpy库，用来存储和处理大型矩阵，比python自带的嵌套列表更高效。numpy库还包含了许多数学函数库。python+numpy等同于matlab。
|
||||
|
||||
def main(): # 主函数的内容放在这里。
|
||||
pass
|
||||
|
||||
if __name__ == '__main__': # 如果是当前文件直接运行，执行main()函数中的内容；如果是import当前文件，则不执行。同时将main()语句放在最后运行，可以避免书写的函数出现未定义的情况。
|
||||
main()
|
||||
@ -1,4 +1,4 @@
|
||||
1 1.2 2.4
|
||||
2 5.5 3.2
|
||||
3 6.7 7.1
|
||||
1 1.2 2.4
|
||||
2 5.5 3.2
|
||||
3 6.7 7.1
|
||||
4 3.6 4.9
|
||||
@ -1,4 +1,4 @@
|
||||
0 1 2 3 4
|
||||
1 1.3 2.7 6.7 8.3
|
||||
2 4.3 2.9 5.4 7.4
|
||||
0 1 2 3 4
|
||||
1 1.3 2.7 6.7 8.3
|
||||
2 4.3 2.9 5.4 7.4
|
||||
3 9.1 8.2 2.6 3.1
|
||||
@ -1,47 +1,47 @@
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
# import os
|
||||
# os.chdir('D:/data') # 设置路径
|
||||
|
||||
|
||||
def main():
|
||||
x, y = read_one_dimension('1D_data.txt')
|
||||
for dim0 in range(y.shape[1]):
|
||||
plt.plot(x, y[:, dim0], '-k')
|
||||
plt.show()
|
||||
|
||||
|
||||
def read_one_dimension(file_name):
|
||||
f = open(file_name, 'r')
|
||||
text = f.read()
|
||||
f.close()
|
||||
row_list = np.array(text.split('\n')) # 根据“回车”分割成每一行
|
||||
# print('文本格式：')
|
||||
# print(text)
|
||||
# print('row_list:')
|
||||
# print(row_list)
|
||||
# print('column:')
|
||||
dim_column = np.array(row_list[0].split()).shape[0] # 列数
|
||||
x = np.array([])
|
||||
y = np.array([])
|
||||
for row in row_list:
|
||||
column = np.array(row.split()) # 每一行根据“空格”继续分割
|
||||
# print(column)
|
||||
if column.shape[0] != 0: # 解决最后一行空白的问题
|
||||
x = np.append(x, [float(column[0])], axis=0) # 第一列为x数据
|
||||
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)
|
||||
# print('x:')
|
||||
# print(x)
|
||||
# print('y:')
|
||||
# print(y)
|
||||
return x, y
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
# import os
|
||||
# os.chdir('D:/data') # 设置路径
|
||||
|
||||
|
||||
def main():
|
||||
x, y = read_one_dimension('1D_data.txt')
|
||||
for dim0 in range(y.shape[1]):
|
||||
plt.plot(x, y[:, dim0], '-k')
|
||||
plt.show()
|
||||
|
||||
|
||||
def read_one_dimension(file_name):
|
||||
f = open(file_name, 'r')
|
||||
text = f.read()
|
||||
f.close()
|
||||
row_list = np.array(text.split('\n')) # 根据“回车”分割成每一行
|
||||
# print('文本格式：')
|
||||
# print(text)
|
||||
# print('row_list:')
|
||||
# print(row_list)
|
||||
# print('column:')
|
||||
dim_column = np.array(row_list[0].split()).shape[0] # 列数
|
||||
x = np.array([])
|
||||
y = np.array([])
|
||||
for row in row_list:
|
||||
column = np.array(row.split()) # 每一行根据“空格”继续分割
|
||||
# print(column)
|
||||
if column.shape[0] != 0: # 解决最后一行空白的问题
|
||||
x = np.append(x, [float(column[0])], axis=0) # 第一列为x数据
|
||||
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)
|
||||
# print('x:')
|
||||
# print(x)
|
||||
# print('y:')
|
||||
# print(y)
|
||||
return x, y
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@ -1,69 +1,69 @@
|
||||
import numpy as np
|
||||
# import os
|
||||
# os.chdir('D:/data') # 设置路径
|
||||
|
||||
|
||||
def main():
|
||||
x1, x2, matrix = read_two_dimension('2D_data.txt')
|
||||
plot_matrix(x1, x2, matrix)
|
||||
|
||||
|
||||
def read_two_dimension(file_name):
|
||||
f = open(file_name, 'r')
|
||||
text = f.read()
|
||||
f.close()
|
||||
row_list = np.array(text.split('\n')) # 根据“回车”分割成每一行
|
||||
# print('文本格式：')
|
||||
# print(text)
|
||||
# print('row_list:')
|
||||
# print(row_list)
|
||||
# print('column:')
|
||||
dim_column = np.array(row_list[0].split()).shape[0] # 列数
|
||||
x1 = np.array([])
|
||||
x2 = np.array([])
|
||||
matrix = np.array([])
|
||||
for i0 in range(row_list.shape[0]):
|
||||
column = np.array(row_list[i0].split()) # 每一行根据“空格”继续分割
|
||||
# print(column)
|
||||
if i0 == 0:
|
||||
x1_str = column[1::] # x1坐标（去除第一个在角落的值）
|
||||
x1 = np.zeros(x1_str.shape[0])
|
||||
for i00 in range(x1_str.shape[0]):
|
||||
x1[i00] = float(x1_str[i00]) # 字符串转浮点
|
||||
elif column.shape[0] != 0: # 解决最后一行空白的问题
|
||||
x2 = np.append(x2, [float(column[0])], axis=0) # 第一列为x数据
|
||||
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)
|
||||
# print('x1:')
|
||||
# print(x1)
|
||||
# print('x2:')
|
||||
# print(x2)
|
||||
# print('matrix:')
|
||||
# print(matrix)
|
||||
return x1, x2, matrix
|
||||
|
||||
|
||||
|
||||
def plot_matrix(x1, x2, matrix):
|
||||
import matplotlib.pyplot as plt
|
||||
from mpl_toolkits.mplot3d import Axes3D
|
||||
from matplotlib import cm
|
||||
from matplotlib.ticker import LinearLocator, FormatStrFormatter
|
||||
fig = plt.figure()
|
||||
ax = fig.gca(projection='3d')
|
||||
x1, x2 = np.meshgrid(x1, x2)
|
||||
ax.plot_surface(x1, x2, matrix, cmap=cm.coolwarm, linewidth=0, antialiased=False)
|
||||
plt.xlabel('x1')
|
||||
plt.ylabel('x2')
|
||||
ax.set_zlabel('z')
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
import numpy as np
|
||||
# import os
|
||||
# os.chdir('D:/data') # 设置路径
|
||||
|
||||
|
||||
def main():
|
||||
x1, x2, matrix = read_two_dimension('2D_data.txt')
|
||||
plot_matrix(x1, x2, matrix)
|
||||
|
||||
|
||||
def read_two_dimension(file_name):
|
||||
f = open(file_name, 'r')
|
||||
text = f.read()
|
||||
f.close()
|
||||
row_list = np.array(text.split('\n')) # 根据“回车”分割成每一行
|
||||
# print('文本格式：')
|
||||
# print(text)
|
||||
# print('row_list:')
|
||||
# print(row_list)
|
||||
# print('column:')
|
||||
dim_column = np.array(row_list[0].split()).shape[0] # 列数
|
||||
x1 = np.array([])
|
||||
x2 = np.array([])
|
||||
matrix = np.array([])
|
||||
for i0 in range(row_list.shape[0]):
|
||||
column = np.array(row_list[i0].split()) # 每一行根据“空格”继续分割
|
||||
# print(column)
|
||||
if i0 == 0:
|
||||
x1_str = column[1::] # x1坐标（去除第一个在角落的值）
|
||||
x1 = np.zeros(x1_str.shape[0])
|
||||
for i00 in range(x1_str.shape[0]):
|
||||
x1[i00] = float(x1_str[i00]) # 字符串转浮点
|
||||
elif column.shape[0] != 0: # 解决最后一行空白的问题
|
||||
x2 = np.append(x2, [float(column[0])], axis=0) # 第一列为x数据
|
||||
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)
|
||||
# print('x1:')
|
||||
# print(x1)
|
||||
# print('x2:')
|
||||
# print(x2)
|
||||
# print('matrix:')
|
||||
# print(matrix)
|
||||
return x1, x2, matrix
|
||||
|
||||
|
||||
|
||||
def plot_matrix(x1, x2, matrix):
|
||||
import matplotlib.pyplot as plt
|
||||
from mpl_toolkits.mplot3d import Axes3D
|
||||
from matplotlib import cm
|
||||
from matplotlib.ticker import LinearLocator, FormatStrFormatter
|
||||
fig = plt.figure()
|
||||
ax = fig.gca(projection='3d')
|
||||
x1, x2 = np.meshgrid(x1, x2)
|
||||
ax.plot_surface(x1, x2, matrix, cmap=cm.coolwarm, linewidth=0, antialiased=False)
|
||||
plt.xlabel('x1')
|
||||
plt.ylabel('x2')
|
||||
ax.set_zlabel('z')
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@ -1,18 +1,18 @@
|
||||
import numpy as np
|
||||
from math import *
|
||||
# import os
|
||||
# os.chdir('D:/data') # 设置路径
|
||||
|
||||
|
||||
f = open('a.txt', 'w')
|
||||
f.write('0 ')
|
||||
for k1 in np.arange(-pi, pi, 0.05):
|
||||
f.write(str(k1)+' ')
|
||||
f.write('\n')
|
||||
for k2 in np.arange(-pi, pi, 0.05):
|
||||
f.write(str(k2)+' ')
|
||||
for k1 in np.arange(-pi, pi, 0.05):
|
||||
data = 1000 # 运算数据
|
||||
f.write(str(data)+' ')
|
||||
f.write('\n')
|
||||
import numpy as np
|
||||
from math import *
|
||||
# import os
|
||||
# os.chdir('D:/data') # 设置路径
|
||||
|
||||
|
||||
f = open('a.txt', 'w')
|
||||
f.write('0 ')
|
||||
for k1 in np.arange(-pi, pi, 0.05):
|
||||
f.write(str(k1)+' ')
|
||||
f.write('\n')
|
||||
for k2 in np.arange(-pi, pi, 0.05):
|
||||
f.write(str(k2)+' ')
|
||||
for k1 in np.arange(-pi, pi, 0.05):
|
||||
data = 1000 # 运算数据
|
||||
f.write(str(data)+' ')
|
||||
f.write('\n')
|
||||
f.close()
|
||||
@ -1,32 +1,32 @@
|
||||
import numpy as np
|
||||
# import os
|
||||
# os.chdir('D:/data') # 设置文件保存的位置
|
||||
|
||||
|
||||
def main():
|
||||
x = [4, 3, 5, 7]
|
||||
y = [6, 1, 3, 2]
|
||||
value = [3, 1, 10, 2]
|
||||
Plot_2D_Scatter(x, y, value, title='Plot 2D Scatter')
|
||||
|
||||
|
||||
def Plot_2D_Scatter(x, y, value, xlabel='x', ylabel='y', title='title', filename='a'):
|
||||
import matplotlib.pyplot as plt
|
||||
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) # 设置刻度值字体大小
|
||||
labels = ax.get_xticklabels() + ax.get_yticklabels()
|
||||
[label.set_fontname('Times New Roman') for label in labels] # 设置刻度值字体
|
||||
# plt.savefig(filename+'.jpg', dpi=300)
|
||||
plt.show()
|
||||
plt.close('all')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
import numpy as np
|
||||
# import os
|
||||
# os.chdir('D:/data') # 设置文件保存的位置
|
||||
|
||||
|
||||
def main():
|
||||
x = [4, 3, 5, 7]
|
||||
y = [6, 1, 3, 2]
|
||||
value = [3, 1, 10, 2]
|
||||
Plot_2D_Scatter(x, y, value, title='Plot 2D Scatter')
|
||||
|
||||
|
||||
def Plot_2D_Scatter(x, y, value, xlabel='x', ylabel='y', title='title', filename='a'):
|
||||
import matplotlib.pyplot as plt
|
||||
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) # 设置刻度值字体大小
|
||||
labels = ax.get_xticklabels() + ax.get_yticklabels()
|
||||
[label.set_fontname('Times New Roman') for label in labels] # 设置刻度值字体
|
||||
# plt.savefig(filename+'.jpg', dpi=300)
|
||||
plt.show()
|
||||
plt.close('all')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@ -1,38 +1,38 @@
|
||||
import numpy as np
|
||||
# import os
|
||||
# os.chdir('D:/data') # 设置文件保存的位置
|
||||
|
||||
|
||||
def main():
|
||||
x = [1, 3, 5, 7]
|
||||
y = [2, 4, 6, 8]
|
||||
z = [2, 8, 6, 1]
|
||||
value = [3, 1, 10, 2]
|
||||
Plot_3D_Scatter(x, y, z, value, title='Plot 3D Scatter')
|
||||
|
||||
|
||||
def Plot_3D_Scatter(x, y, z, value, xlabel='x', ylabel='y', zlabel='z', title='title', filename='a'):
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib.ticker import LinearLocator
|
||||
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.set_zlim(0, 20)
|
||||
# ax.zaxis.set_major_locator(LinearLocator(6)) # 设置z轴主刻度的个数
|
||||
# ax.zaxis.set_major_formatter('{x:.0f}') # 设置z轴主刻度的格式
|
||||
ax.tick_params(labelsize=15) # 设置刻度值字体大小
|
||||
labels = ax.get_xticklabels() + ax.get_yticklabels() + ax.get_zticklabels()
|
||||
[label.set_fontname('Times New Roman') for label in labels] # 设置刻度值字体
|
||||
# plt.savefig(filename+'.jpg', dpi=300)
|
||||
plt.show()
|
||||
plt.close('all')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
import numpy as np
|
||||
# import os
|
||||
# os.chdir('D:/data') # 设置文件保存的位置
|
||||
|
||||
|
||||
def main():
|
||||
x = [1, 3, 5, 7]
|
||||
y = [2, 4, 6, 8]
|
||||
z = [2, 8, 6, 1]
|
||||
value = [3, 1, 10, 2]
|
||||
Plot_3D_Scatter(x, y, z, value, title='Plot 3D Scatter')
|
||||
|
||||
|
||||
def Plot_3D_Scatter(x, y, z, value, xlabel='x', ylabel='y', zlabel='z', title='title', filename='a'):
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib.ticker import LinearLocator
|
||||
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.set_zlim(0, 20)
|
||||
# ax.zaxis.set_major_locator(LinearLocator(6)) # 设置z轴主刻度的个数
|
||||
# ax.zaxis.set_major_formatter('{x:.0f}') # 设置z轴主刻度的格式
|
||||
ax.tick_params(labelsize=15) # 设置刻度值字体大小
|
||||
labels = ax.get_xticklabels() + ax.get_yticklabels() + ax.get_zticklabels()
|
||||
[label.set_fontname('Times New Roman') for label in labels] # 设置刻度值字体
|
||||
# plt.savefig(filename+'.jpg', dpi=300)
|
||||
plt.show()
|
||||
plt.close('all')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@ -1,44 +1,44 @@
|
||||
import numpy as np
|
||||
# import os
|
||||
# os.chdir('D:/data') # 设置文件保存的位置
|
||||
|
||||
|
||||
def main():
|
||||
x = np.arange(-5, 5, 0.25)
|
||||
y = np.arange(-5, 5, 0.25)
|
||||
X, Y = np.meshgrid(x, y)
|
||||
R = np.sqrt(X**2 + Y**2)
|
||||
Z = np.sin(R)
|
||||
Plot_3D_Surface(x,y,Z)
|
||||
|
||||
|
||||
def Plot_3D_Surface(x,y,matrix,filename='a.jpg', titlename='Plot 3D Surface'):
|
||||
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) # 调整位置
|
||||
x, y = np.meshgrid(x, y)
|
||||
surf = ax.plot_surface(x, y, matrix, cmap=cm.coolwarm, linewidth=0, antialiased=False) # Plot the surface.
|
||||
ax.set_title(titlename, fontsize=20, fontfamily='Times New Roman')
|
||||
ax.set_xlabel('x', fontsize=30, fontfamily='Times New Roman') # 坐标标签
|
||||
ax.set_ylabel('y', fontsize=30, fontfamily='Times New Roman') # 坐标标签
|
||||
ax.set_zlabel('z', fontsize=30, fontfamily='Times New Roman') # 坐标标签
|
||||
# ax.set_zlim(-1, 1) # 设置z轴的范围
|
||||
ax.zaxis.set_major_locator(LinearLocator(5)) # 设置z轴主刻度的个数
|
||||
ax.zaxis.set_major_formatter('{x:.2f}') # 设置z轴主刻度的格式
|
||||
ax.tick_params(labelsize=15) # 设置刻度值字体大小
|
||||
labels = ax.get_xticklabels() + ax.get_yticklabels() + ax.get_zticklabels()
|
||||
[label.set_fontname('Times New Roman') for label in labels] # 设置刻度值字体
|
||||
cax = plt.axes([0.75, 0.15, 0.05, 0.75]) # color bar的位置 [左，下，宽度， 高度]
|
||||
cbar = fig.colorbar(surf, cax=cax) # color bar
|
||||
cbar.ax.tick_params(labelsize=15) # 设置color bar刻度的字体大小
|
||||
for l in cbar.ax.yaxis.get_ticklabels(): # 设置color bar刻度的字体
|
||||
l.set_family('Times New Roman')
|
||||
# plt.savefig(filename, dpi=800) # 保存图片文件
|
||||
plt.show()
|
||||
plt.close('all') # 关闭所有plt，防止循环画图时占用内存
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
import numpy as np
|
||||
# import os
|
||||
# os.chdir('D:/data') # 设置文件保存的位置
|
||||
|
||||
|
||||
def main():
|
||||
x = np.arange(-5, 5, 0.25)
|
||||
y = np.arange(-5, 5, 0.25)
|
||||
X, Y = np.meshgrid(x, y)
|
||||
R = np.sqrt(X**2 + Y**2)
|
||||
Z = np.sin(R)
|
||||
Plot_3D_Surface(x,y,Z)
|
||||
|
||||
|
||||
def Plot_3D_Surface(x,y,matrix,filename='a.jpg', titlename='Plot 3D Surface'):
|
||||
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) # 调整位置
|
||||
x, y = np.meshgrid(x, y)
|
||||
surf = ax.plot_surface(x, y, matrix, cmap=cm.coolwarm, linewidth=0, antialiased=False) # Plot the surface.
|
||||
ax.set_title(titlename, fontsize=20, fontfamily='Times New Roman')
|
||||
ax.set_xlabel('x', fontsize=30, fontfamily='Times New Roman') # 坐标标签
|
||||
ax.set_ylabel('y', fontsize=30, fontfamily='Times New Roman') # 坐标标签
|
||||
ax.set_zlabel('z', fontsize=30, fontfamily='Times New Roman') # 坐标标签
|
||||
# ax.set_zlim(-1, 1) # 设置z轴的范围
|
||||
ax.zaxis.set_major_locator(LinearLocator(5)) # 设置z轴主刻度的个数
|
||||
ax.zaxis.set_major_formatter('{x:.2f}') # 设置z轴主刻度的格式
|
||||
ax.tick_params(labelsize=15) # 设置刻度值字体大小
|
||||
labels = ax.get_xticklabels() + ax.get_yticklabels() + ax.get_zticklabels()
|
||||
[label.set_fontname('Times New Roman') for label in labels] # 设置刻度值字体
|
||||
cax = plt.axes([0.75, 0.15, 0.05, 0.75]) # color bar的位置 [左，下，宽度， 高度]
|
||||
cbar = fig.colorbar(surf, cax=cax) # color bar
|
||||
cbar.ax.tick_params(labelsize=15) # 设置color bar刻度的字体大小
|
||||
for l in cbar.ax.yaxis.get_ticklabels(): # 设置color bar刻度的字体
|
||||
l.set_family('Times New Roman')
|
||||
# plt.savefig(filename, dpi=800) # 保存图片文件
|
||||
plt.show()
|
||||
plt.close('all') # 关闭所有plt，防止循环画图时占用内存
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
82
academic_codes/2020.12.23_plot_with_matplotlib/plot_contour.py → language_learning/2020.12.23_plot_with_matplotlib/plot_contour.py
Executable file → Normal file
82
academic_codes/2020.12.23_plot_with_matplotlib/plot_contour.py → language_learning/2020.12.23_plot_with_matplotlib/plot_contour.py
Executable file → Normal file
@ -1,42 +1,42 @@
|
||||
import numpy as np
|
||||
# import os
|
||||
# os.chdir('D:/data') # 设置文件保存的位置
|
||||
|
||||
|
||||
def main():
|
||||
x = np.arange(-5, 5, 0.25)
|
||||
y = np.arange(-5, 5, 0.25)
|
||||
X, Y = np.meshgrid(x, y)
|
||||
R = np.sqrt(X**2 + Y**2)
|
||||
Z = np.sin(R)
|
||||
Plot_Contour(x,y,Z)
|
||||
|
||||
|
||||
def Plot_Contour(x,y,matrix,filename='a.jpg', titlename='Plot Contour'):
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib import cm
|
||||
from matplotlib.ticker import LinearLocator
|
||||
fig, ax = plt.subplots()
|
||||
plt.subplots_adjust(bottom=0.15, right=0.7) # 调整位置
|
||||
x, y = np.meshgrid(x, y)
|
||||
contour = ax.contourf(x,y,matrix,cmap='jet')
|
||||
ax.set_title(titlename, fontsize=20, fontfamily='Times New Roman')
|
||||
ax.set_xlabel('x', fontsize=30, fontfamily='Times New Roman') # 坐标标签
|
||||
ax.set_ylabel('y', fontsize=30, fontfamily='Times New Roman') # 坐标标签
|
||||
# plt.xlabel('x')
|
||||
# plt.ylabel('y')
|
||||
ax.tick_params(labelsize=15) # 设置刻度值字体大小
|
||||
labels = ax.get_xticklabels() + ax.get_yticklabels()
|
||||
[label.set_fontname('Times New Roman') for label in labels] # 设置刻度值字体
|
||||
cax = plt.axes([0.75, 0.15, 0.08, 0.73]) # color bar的位置 [左，下，宽度， 高度]
|
||||
cbar = fig.colorbar(contour, cax=cax) # color bar
|
||||
cbar.ax.tick_params(labelsize=15) # 设置color bar刻度的字体大小
|
||||
for l in cbar.ax.yaxis.get_ticklabels(): # 设置color bar刻度的字体
|
||||
l.set_family('Times New Roman')
|
||||
# plt.savefig(filename, dpi=800) # 保存图片文件
|
||||
plt.show()
|
||||
plt.close('all') # 关闭所有plt，防止循环画图时占用内存
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
import numpy as np
|
||||
# import os
|
||||
# os.chdir('D:/data') # 设置文件保存的位置
|
||||
|
||||
|
||||
def main():
|
||||
x = np.arange(-5, 5, 0.25)
|
||||
y = np.arange(-5, 5, 0.25)
|
||||
X, Y = np.meshgrid(x, y)
|
||||
R = np.sqrt(X**2 + Y**2)
|
||||
Z = np.sin(R)
|
||||
Plot_Contour(x,y,Z)
|
||||
|
||||
|
||||
def Plot_Contour(x,y,matrix,filename='a.jpg', titlename='Plot Contour'):
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib import cm
|
||||
from matplotlib.ticker import LinearLocator
|
||||
fig, ax = plt.subplots()
|
||||
plt.subplots_adjust(bottom=0.15, right=0.7) # 调整位置
|
||||
x, y = np.meshgrid(x, y)
|
||||
contour = ax.contourf(x,y,matrix,cmap='jet')
|
||||
ax.set_title(titlename, fontsize=20, fontfamily='Times New Roman')
|
||||
ax.set_xlabel('x', fontsize=30, fontfamily='Times New Roman') # 坐标标签
|
||||
ax.set_ylabel('y', fontsize=30, fontfamily='Times New Roman') # 坐标标签
|
||||
# plt.xlabel('x')
|
||||
# plt.ylabel('y')
|
||||
ax.tick_params(labelsize=15) # 设置刻度值字体大小
|
||||
labels = ax.get_xticklabels() + ax.get_yticklabels()
|
||||
[label.set_fontname('Times New Roman') for label in labels] # 设置刻度值字体
|
||||
cax = plt.axes([0.75, 0.15, 0.08, 0.73]) # color bar的位置 [左，下，宽度， 高度]
|
||||
cbar = fig.colorbar(contour, cax=cax) # color bar
|
||||
cbar.ax.tick_params(labelsize=15) # 设置color bar刻度的字体大小
|
||||
for l in cbar.ax.yaxis.get_ticklabels(): # 设置color bar刻度的字体
|
||||
l.set_family('Times New Roman')
|
||||
# plt.savefig(filename, dpi=800) # 保存图片文件
|
||||
plt.show()
|
||||
plt.close('all') # 关闭所有plt，防止循环画图时占用内存
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
58
academic_codes/2020.12.23_plot_with_matplotlib/plot_line.py → language_learning/2020.12.23_plot_with_matplotlib/plot_line.py
Executable file → Normal file
58
academic_codes/2020.12.23_plot_with_matplotlib/plot_line.py → language_learning/2020.12.23_plot_with_matplotlib/plot_line.py
Executable file → Normal file
@ -1,30 +1,30 @@
|
||||
import numpy as np
|
||||
# import os
|
||||
# os.chdir('D:/data') # 设置文件保存的位置
|
||||
|
||||
|
||||
def main():
|
||||
x = np.arange(0.0, 2.0, 0.01)
|
||||
y = 1 + np.sin(2 * np.pi * x)
|
||||
Plot_Line(x,y)
|
||||
|
||||
|
||||
def Plot_Line(x,y,filename='a.jpg', titlename='Plot Line'):
|
||||
import matplotlib.pyplot as plt
|
||||
fig, ax = plt.subplots()
|
||||
plt.subplots_adjust(bottom=0.20, left=0.16)
|
||||
ax.plot(x, y, '-o')
|
||||
ax.grid()
|
||||
ax.set_title(titlename, fontsize=20, fontfamily='Times New Roman')
|
||||
ax.set_xlabel('x', fontsize=30, fontfamily='Times New Roman') # 坐标标签
|
||||
ax.set_ylabel('y', fontsize=30, fontfamily='Times New Roman') # 坐标标签
|
||||
ax.tick_params(labelsize=20) # 设置刻度值字体大小
|
||||
labels = ax.get_xticklabels() + ax.get_yticklabels()
|
||||
[label.set_fontname('Times New Roman') for label in labels] # 设置刻度值字体
|
||||
# plt.savefig(filename, dpi=800) # 保存图片文件
|
||||
plt.show()
|
||||
plt.close('all') # 关闭所有plt，防止循环画图时占用内存
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
import numpy as np
|
||||
# import os
|
||||
# os.chdir('D:/data') # 设置文件保存的位置
|
||||
|
||||
|
||||
def main():
|
||||
x = np.arange(0.0, 2.0, 0.01)
|
||||
y = 1 + np.sin(2 * np.pi * x)
|
||||
Plot_Line(x,y)
|
||||
|
||||
|
||||
def Plot_Line(x,y,filename='a.jpg', titlename='Plot Line'):
|
||||
import matplotlib.pyplot as plt
|
||||
fig, ax = plt.subplots()
|
||||
plt.subplots_adjust(bottom=0.20, left=0.16)
|
||||
ax.plot(x, y, '-o')
|
||||
ax.grid()
|
||||
ax.set_title(titlename, fontsize=20, fontfamily='Times New Roman')
|
||||
ax.set_xlabel('x', fontsize=30, fontfamily='Times New Roman') # 坐标标签
|
||||
ax.set_ylabel('y', fontsize=30, fontfamily='Times New Roman') # 坐标标签
|
||||
ax.tick_params(labelsize=20) # 设置刻度值字体大小
|
||||
labels = ax.get_xticklabels() + ax.get_yticklabels()
|
||||
[label.set_fontname('Times New Roman') for label in labels] # 设置刻度值字体
|
||||
# plt.savefig(filename, dpi=800) # 保存图片文件
|
||||
plt.show()
|
||||
plt.close('all') # 关闭所有plt，防止循环画图时占用内存
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@ -1,322 +1,322 @@
|
||||
"""
|
||||
This code is supported by the website: https://www.guanjihuan.com
|
||||
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__":
|
||||
"""
|
||||
This code is supported by the website: https://www.guanjihuan.com
|
||||
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__":
|
||||
main()
|
||||
Loading…
x
Reference in New Issue
Block a user