0.1.177

PyPI/setup.cfg

@@ -1,7 +1,7 @@
 [metadata]
 # replace with your username:
 name = guan
-version = 0.1.176
+version = 0.1.177
 author = guanjihuan
 author_email = guanjihuan@163.com
 description = An open source python package

PyPI/src/guan.egg-info/PKG-INFO

@@ -1,6 +1,6 @@
 Metadata-Version: 2.4
 Name: guan
-Version: 0.1.176
+Version: 0.1.177
 Summary: An open source python package
 Home-page: https://py.guanjihuan.com
 Author: guanjihuan

PyPI/src/guan/band_structures_and_wave_functions.py

@@ -1,12 +1,15 @@
 # Module: band_structures_and_wave_functions
 
 # 计算哈密顿量的本征值
-def calculate_eigenvalue(hamiltonian):
+def calculate_eigenvalue(hamiltonian, hermitian=True):
     import numpy as np
     if np.array(hamiltonian).shape==():
         eigenvalue = np.real(hamiltonian)
     else:
-        eigenvalue, eigenvector = np.linalg.eigh(hamiltonian)
+        if hermitian:
+            eigenvalue, eigenvector = np.linalg.eigh(hamiltonian)
+        else:
+            eigenvalue, eigenvector = np.linalg.eig(hamiltonian)
     return eigenvalue
 
 # 输入哈密顿量函数（带一组参数），计算一组参数下的本征值，返回本征值向量组
@@ -65,10 +68,13 @@ def calculate_eigenvalue_with_two_parameters(x_array, y_array, hamiltonian_funct
             i0 += 1
     return eigenvalue_array
 
-# 计算哈密顿量的本征矢（厄密矩阵）
-def calculate_eigenvector(hamiltonian):
+# 计算哈密顿量的本征矢
+def calculate_eigenvector(hamiltonian, hermitian=True):
     import numpy as np
-    eigenvalue, eigenvector = np.linalg.eigh(hamiltonian)
+    if hermitian:
+        eigenvalue, eigenvector = np.linalg.eigh(hamiltonian)
+    else:
+        eigenvalue, eigenvector = np.linalg.eig(hamiltonian)
     return eigenvector
 
 # 施密特正交化

PyPI/src/guan/file_reading_and_writing.py

@@ -1,7 +1,7 @@
 # Module: file_reading_and_writing
 
 # 使用pickle将变量保存到文件（支持几乎所有对象类型）
-def dump_data(data, filename, file_format='.pkl'):
+def dump_data(data, filename='a', file_format='.pkl'):
     import pickle
     with open(filename+file_format, 'wb') as f:
         pickle.dump(data, f)
@@ -14,7 +14,7 @@ def load_data(filename, file_format='.pkl'):
     return data
 
 # 使用NumPy保存数组变量到npy文件（二进制文件）
-def save_npy_data(data, filename):
+def save_npy_data(data, filename='a'):
     import numpy as np
     np.save(filename+'.npy', data)
 
@@ -25,7 +25,7 @@ def load_npy_data(filename):
     return data
 
 # 使用NumPy保存数组变量到TXT文件（文本文件）
-def save_txt_data(data, filename):
+def save_txt_data(data, filename='a'):
     import numpy as np
     np.savetxt(filename+'.txt', data)
 
@@ -421,13 +421,15 @@ def write_matrix_in_markdown_format(matrix, filename='a'):
     dim_0 = matrix.shape[0]
     dim_1 = matrix.shape[1]
     with open(filename+'.md', 'w', encoding='UTF-8') as f:
+        f.write(f'| Row\Column ')
         for i1 in range(dim_1):
             f.write(f'| column {i1+1} ')
         f.write('|\n')
-        for i1 in range(dim_1):
+        for i1 in range(dim_1+1):
             f.write('| :---: ')
         f.write('|\n')
         for i0 in range(dim_0):
+            f.write(f'| row {i0+1} ')
             for i1 in range(dim_1):
                 f.write(f'| {matrix[i0, i1]} ')
             f.write('|\n')