0.1.126

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

@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: guan
-Version: 0.1.125
+Version: 0.1.126
 Summary: An open source python package
 Home-page: https://py.guanjihuan.com
 Author: guanjihuan

PyPI/src/guan/band_structures_and_wave_functions.py

@@ -65,12 +65,47 @@ 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
+# 计算哈密顿量的本征矢（厄密矩阵）
 def calculate_eigenvector(hamiltonian):
     import numpy as np
     eigenvalue, eigenvector = np.linalg.eigh(hamiltonian)
     return eigenvector
 
+# 施密特正交化
+def schmidt_orthogonalization(eigenvector):
+    import numpy as np
+    num = eigenvector.shape[1]
+    for i in range(num):
+        for i0 in range(i):
+            eigenvector[:, i] = eigenvector[:, i] - eigenvector[:, i0]*np.dot(eigenvector[:, i].transpose().conj(), eigenvector[:, i0])/(np.dot(eigenvector[:, i0].transpose().conj(),eigenvector[:, i0]))
+        eigenvector[:, i] = eigenvector[:, i]/np.linalg.norm(eigenvector[:, i])
+    return eigenvector
+
+# 通过QR分解正交化
+def orthogonalization_with_qr(eigenvector):
+    import numpy as np
+    Q, R = np.linalg.qr(eigenvector)
+    return Q
+
+# 通过SVD正交化
+def orthogonalization_with_svd(eigenvector):
+    import numpy as np
+    U, sigma, VT = np.linalg.svd(eigenvector)
+    return U
+
+# 通过scipy.linalg.orth正交化
+def orthogonalization_with_scipy_linalg_orth(eigenvector):
+    import scipy
+    Q = scipy.linalg.orth(eigenvector)
+    return Q
+
+# 验证是否正交
+def verify_orthogonality(eigenvector):
+    import numpy as np
+    identity = np.eye(eigenvector.shape[1])
+    product = np.dot(eigenvector.transpose().conj(), eigenvector)
+    return np.allclose(product, identity)
+
 # 通过二分查找的方法获取和相邻波函数一样规范的波函数
 def find_vector_with_the_same_gauge_with_binary_search(vector_target, vector_ref, show_error=1, show_times=0, show_phase=0, n_test=1000, precision=1e-6):
     import numpy as np

PyPI/src/guan/others.py

@@ -481,6 +481,11 @@ def convert_wordpress_xml_to_markdown(xml_file='./a.xml', convert_content=1, rep
         with open(cleaned_filename, 'w', encoding='utf-8') as md_file:
             md_file.write(markdown_content)
 
+# 凯利公式
+def kelly_formula(p, b, a=1):
+    f=(p/a)-((1-p)/b)
+    return f
+
 # 获取所有股票
 def all_stocks():
     import numpy as np