diff --git a/API_Reference/API_Reference.py b/API_Reference/API_Reference.py
index 9a81836..bcb1303 100644
--- a/API_Reference/API_Reference.py
+++ b/API_Reference/API_Reference.py
@@ -12,6 +12,13 @@ import guan
 
 
 
+
+
+
+
+
+
+
 
 
 
@@ -229,8 +236,7 @@ hamiltonian = guan.hamiltonian_of_cubic_lattice(k1, k2, k3)
 hamiltonian = guan.hamiltonian_of_ssh_model(k, v=0.6, w=1)
 
 # 石墨烯的哈密顿量
-import math
-hamiltonian = guan.hamiltonian_of_graphene(k1, k2, staggered_potential=0, t=1, a=1/math.sqrt(3))
+hamiltonian = guan.hamiltonian_of_graphene(k1, k2, staggered_potential=0, t=1, a='default')
 
 # 石墨烯有效模型的哈密顿量
 hamiltonian = guan.effective_hamiltonian_of_graphene(qx, qy, t=1, staggered_potential=0, valley_index=0)
@@ -242,12 +248,10 @@ hamiltonian = guan.effective_hamiltonian_of_graphene_after_discretization(qx, qy
 hamiltonian = guan.hamiltonian_of_graphene_with_zigzag_in_quasi_one_dimension(k, N=10, M=0, t=1, period=0)
 
 # Haldane模型的哈密顿量
-import math
-hamiltonian = guan.hamiltonian_of_haldane_model(k1, k2, M=2/3, t1=1, t2=1/3, phi=math.pi/4, a=1/math.sqrt(3))
+hamiltonian = guan.hamiltonian_of_haldane_model(k1, k2, M=2/3, t1=1, t2=1/3, phi=math.pi/4, a='default')
 
 # 准一维Haldane模型条带的哈密顿量
-import math
-hamiltonian = guan.hamiltonian_of_haldane_model_in_quasi_one_dimension(k, N=10, M=2/3, t1=1, t2=1/3, phi=math.pi/4, period=0)
+hamiltonian = guan.hamiltonian_of_haldane_model_in_quasi_one_dimension(k, N=10, M=2/3, t1=1, t2=1/3, phi='default', period=0)
 
 # 一个量子反常霍尔效应的哈密顿量
 hamiltonian = guan.hamiltonian_of_one_QAH_model(k1, k2, t1=1, t2=1, t3=0.5, m=-1)
@@ -583,27 +587,24 @@ chern_number = guan.calculate_chern_number_for_square_lattice_with_wilson_loop(h
 chern_number = guan.calculate_chern_number_for_square_lattice_with_wilson_loop_for_degenerate_case(hamiltonian_function, index_of_bands=[0, 1], precision_of_plaquettes=20, precision_of_wilson_loop=5, print_show=0)
 
 # 通过高效法计算贝利曲率
-import math
-k_array, berry_curvature_array = guan.calculate_berry_curvature_with_efficient_method(hamiltonian_function, k_min=-math.pi, k_max=math.pi, precision=100, print_show=0)
+k_array, berry_curvature_array = guan.calculate_berry_curvature_with_efficient_method(hamiltonian_function, k_min='default', k_max='default', precision=100, print_show=0)
 
 # 通过高效法计算贝利曲率（可计算简并的情况）
-import math
-k_array, berry_curvature_array = guan.calculate_berry_curvature_with_efficient_method_for_degenerate_case(hamiltonian_function, index_of_bands=[0, 1], k_min=-math.pi, k_max=math.pi, precision=100, print_show=0)
+k_array, berry_curvature_array = guan.calculate_berry_curvature_with_efficient_method_for_degenerate_case(hamiltonian_function, index_of_bands=[0, 1], k_min='default', k_max='default', precision=100, print_show=0)
 
 # 通过Wilson loop方法计算贝里曲率
-import math
-k_array, berry_curvature_array = guan.calculate_berry_curvature_with_wilson_loop(hamiltonian_function, k_min=-math.pi, k_max=math.pi, precision_of_plaquettes=20, precision_of_wilson_loop=5, print_show=0)
+k_array, berry_curvature_array = guan.calculate_berry_curvature_with_wilson_loop(hamiltonian_function, k_min='default', k_max='default', precision_of_plaquettes=20, precision_of_wilson_loop=5, print_show=0)
 
 # 通过Wilson loop方法计算贝里曲率（可计算简并的情况）
-import math
-k_array, berry_curvature_array = guan.calculate_berry_curvature_with_wilson_loop_for_degenerate_case(hamiltonian_function, index_of_bands=[0, 1], k_min=-math.pi, k_max=math.pi, precision_of_plaquettes=20, precision_of_wilson_loop=5, print_show=0)
+k_array, berry_curvature_array = guan.calculate_berry_curvature_with_wilson_loop_for_degenerate_case(hamiltonian_function, index_of_bands=[0, 1], k_min='default', k_max='default', precision_of_plaquettes=20, precision_of_wilson_loop=5, print_show=0)
 
 # 计算蜂窝格子的陈数（高效法）
 chern_number = guan.calculate_chern_number_for_honeycomb_lattice(hamiltonian_function, a=1, precision=300, print_show=0)
 
 # 计算Wilson loop
-import math
-wilson_loop_array = guan.calculate_wilson_loop(hamiltonian_function, k_min=-math.pi, k_max=math.pi, precision=100, print_show=0)
+wilson_loop_array = guan.calculate_wilson_loop(hamiltonian_function, k_min='default', k_max='default', precision=100, print_show=0)
+
+
 
 
 
@@ -779,6 +780,13 @@ guan.print_array_with_index(array, show_index=1, index_type=0)
 
 
 
+
+
+
+
+
+
+
 
 
 
@@ -856,6 +864,10 @@ guan.play_element_words(random_on=0, show_translation=1, show_link=1, translatio
 
 
 
+
+
+
+
 
 
 
diff --git a/Source_Code/PyPI/setup.cfg b/Source_Code/PyPI/setup.cfg
index af08205..f0b95d9 100644
--- a/Source_Code/PyPI/setup.cfg
+++ b/Source_Code/PyPI/setup.cfg
@@ -1,7 +1,7 @@
 [metadata]
 # replace with your username:
 name = guan
-version = 0.0.180
+version = 0.0.181
 author = guanjihuan
 author_email = guanjihuan@163.com
 description = An open source python package
diff --git a/Source_Code/PyPI/src/guan.egg-info/PKG-INFO b/Source_Code/PyPI/src/guan.egg-info/PKG-INFO
index ad8ff50..0b8f35b 100644
--- a/Source_Code/PyPI/src/guan.egg-info/PKG-INFO
+++ b/Source_Code/PyPI/src/guan.egg-info/PKG-INFO
@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: guan
-Version: 0.0.180
+Version: 0.0.181
 Summary: An open source python package
 Home-page: https://py.guanjihuan.com
 Author: guanjihuan
diff --git a/Source_Code/PyPI/src/guan/__init__.py b/Source_Code/PyPI/src/guan/__init__.py
index fcbd96a..113fd83 100644
--- a/Source_Code/PyPI/src/guan/__init__.py
+++ b/Source_Code/PyPI/src/guan/__init__.py
@@ -2,7 +2,7 @@
 
 # With this package, you can calculate band structures, density of states, quantum transport and topological invariant of tight-binding models by invoking the functions you need. Other frequently used functions are also integrated in this package, such as file reading/writing, figure plotting, data processing.
 
-# The current version is guan-0.0.180, updated on December 03, 2023.
+# The current version is guan-0.0.181, updated on December 03, 2023.
 
 # Installation: pip install --upgrade guan
 
@@ -679,11 +679,12 @@ def hamiltonian_of_ssh_model(k, v=0.6, w=1):
     return hamiltonian
 
 # 石墨烯的哈密顿量
-import math
-def hamiltonian_of_graphene(k1, k2, staggered_potential=0, t=1, a=1/math.sqrt(3)):
+def hamiltonian_of_graphene(k1, k2, staggered_potential=0, t=1, a='default'):
     import numpy as np
     import cmath
     import math
+    if a == 'default':
+        a = 1/math.sqrt(3)
     h0 = np.zeros((2, 2), dtype=complex)  # mass term
     h1 = np.zeros((2, 2), dtype=complex)  # nearest hopping
     h0[0, 0] = staggered_potential     
@@ -753,11 +754,12 @@ def hamiltonian_of_graphene_with_zigzag_in_quasi_one_dimension(k, N=10, M=0, t=1
     return hamiltonian
 
 # Haldane模型的哈密顿量
-import math
-def hamiltonian_of_haldane_model(k1, k2, M=2/3, t1=1, t2=1/3, phi=math.pi/4, a=1/math.sqrt(3)):
+def hamiltonian_of_haldane_model(k1, k2, M=2/3, t1=1, t2=1/3, phi=math.pi/4, a='default'):
     import numpy as np
     import cmath
     import math
+    if a == 'default':
+        a=1/math.sqrt(3)
     h0 = np.zeros((2, 2), dtype=complex)  # mass term
     h1 = np.zeros((2, 2), dtype=complex)  # nearest hopping
     h2 = np.zeros((2, 2), dtype=complex)  # next nearest hopping
@@ -771,10 +773,12 @@ def hamiltonian_of_haldane_model(k1, k2, M=2/3, t1=1, t2=1/3, phi=math.pi/4, a=1
     return hamiltonian
 
 # 准一维Haldane模型条带的哈密顿量
-import math
-def hamiltonian_of_haldane_model_in_quasi_one_dimension(k, N=10, M=2/3, t1=1, t2=1/3, phi=math.pi/4, period=0):
+def hamiltonian_of_haldane_model_in_quasi_one_dimension(k, N=10, M=2/3, t1=1, t2=1/3, phi='default', period=0):
     import numpy as np
     import cmath
+    import math
+    if phi == 'default':
+        phi=math.pi/4
     h00 = np.zeros((4*N, 4*N), dtype=complex)  # hopping in a unit cell
     h01 = np.zeros((4*N, 4*N), dtype=complex)  # hopping between unit cells
     for i in range(N):
@@ -2422,11 +2426,15 @@ def calculate_chern_number_for_square_lattice_with_wilson_loop_for_degenerate_ca
     return chern_number
 
 # 通过高效法计算贝利曲率
-import math
-def calculate_berry_curvature_with_efficient_method(hamiltonian_function, k_min=-math.pi, k_max=math.pi, precision=100, print_show=0):
+def calculate_berry_curvature_with_efficient_method(hamiltonian_function, k_min='default', k_max='default', precision=100, print_show=0):
     import numpy as np
     import cmath
     import guan
+    import math
+    if k_min == 'default':
+        k_min = -math.pi
+    if k_max == 'default':
+        k_max=math.pi
     if np.array(hamiltonian_function(0, 0)).shape==():
         dim = 1
     else:
@@ -2464,10 +2472,14 @@ def calculate_berry_curvature_with_efficient_method(hamiltonian_function, k_min=
     return k_array, berry_curvature_array
 
 # 通过高效法计算贝利曲率（可计算简并的情况）
-import math
-def calculate_berry_curvature_with_efficient_method_for_degenerate_case(hamiltonian_function, index_of_bands=[0, 1], k_min=-math.pi, k_max=math.pi, precision=100, print_show=0):
+def calculate_berry_curvature_with_efficient_method_for_degenerate_case(hamiltonian_function, index_of_bands=[0, 1], k_min='default', k_max='default', precision=100, print_show=0):
     import numpy as np
     import cmath
+    import math
+    if k_min == 'default':
+        k_min = -math.pi
+    if k_max == 'default':
+        k_max=math.pi
     delta = (k_max-k_min)/precision
     k_array = np.arange(k_min, k_max, delta)
     berry_curvature_array = np.zeros((k_array.shape[0], k_array.shape[0]), dtype=complex)
@@ -2538,9 +2550,13 @@ def calculate_berry_curvature_with_efficient_method_for_degenerate_case(hamilton
     return k_array, berry_curvature_array
 
 # 通过Wilson loop方法计算贝里曲率
-import math
-def calculate_berry_curvature_with_wilson_loop(hamiltonian_function, k_min=-math.pi, k_max=math.pi, precision_of_plaquettes=20, precision_of_wilson_loop=5, print_show=0):
+def calculate_berry_curvature_with_wilson_loop(hamiltonian_function, k_min='default', k_max='default', precision_of_plaquettes=20, precision_of_wilson_loop=5, print_show=0):
     import numpy as np
+    import math
+    if k_min == 'default':
+        k_min = -math.pi
+    if k_max == 'default':
+        k_max=math.pi
     if np.array(hamiltonian_function(0, 0)).shape==():
         dim = 1
     else:
@@ -2590,9 +2606,13 @@ def calculate_berry_curvature_with_wilson_loop(hamiltonian_function, k_min=-math
     return k_array, berry_curvature_array
 
 # 通过Wilson loop方法计算贝里曲率（可计算简并的情况）
-import math
-def calculate_berry_curvature_with_wilson_loop_for_degenerate_case(hamiltonian_function, index_of_bands=[0, 1], k_min=-math.pi, k_max=math.pi, precision_of_plaquettes=20, precision_of_wilson_loop=5, print_show=0):
+def calculate_berry_curvature_with_wilson_loop_for_degenerate_case(hamiltonian_function, index_of_bands=[0, 1], k_min='default', k_max='default', precision_of_plaquettes=20, precision_of_wilson_loop=5, print_show=0):
     import numpy as np
+    import math
+    if k_min == 'default':
+        k_min = -math.pi
+    if k_max == 'default':
+        k_max=math.pi
     delta = (k_max-k_min)/precision_of_plaquettes
     k_array = np.arange(k_min, k_max, delta)
     berry_curvature_array = np.zeros((k_array.shape[0], k_array.shape[0]), dtype=complex)
@@ -2700,10 +2720,14 @@ def calculate_chern_number_for_honeycomb_lattice(hamiltonian_function, a=1, prec
     return chern_number
 
 # 计算Wilson loop
-import math
-def calculate_wilson_loop(hamiltonian_function, k_min=-math.pi, k_max=math.pi, precision=100, print_show=0):
+def calculate_wilson_loop(hamiltonian_function, k_min='default', k_max='default', precision=100, print_show=0):
     import numpy as np
     import guan
+    import math
+    if k_min == 'default':
+        k_min = -math.pi
+    if k_max == 'default':
+        k_max=math.pi
     k_array = np.linspace(k_min, k_max, precision)
     dim = np.array(hamiltonian_function(0)).shape[0]
     wilson_loop_array = np.ones(dim, dtype=complex)