From 9e811126143e95f857e92f64464cdca4f316ed73 Mon Sep 17 00:00:00 2001
From: guanjihuan <guanjihuan@163.com>
Date: Wed, 20 Jul 2022 15:03:44 +0800
Subject: [PATCH] 0.0.112

---
 API_Reference.py          |  4 ++++
 PyPI/setup.cfg            |  2 +-
 PyPI/src/guan/__init__.py | 28 +++++++++++++++++++++++++++-
 3 files changed, 32 insertions(+), 2 deletions(-)

diff --git a/API_Reference.py b/API_Reference.py
index 2395d8f..4076826 100644
--- a/API_Reference.py
+++ b/API_Reference.py
@@ -111,6 +111,10 @@ hamiltonian = guan.hamiltonian_of_ssh_model(k, v=0.6, w=1)
 
 hamiltonian = guan.hamiltonian_of_graphene(k1, k2, M=0, t=1, a=1/math.sqrt(3))
 
+hamiltonian = guan.effective_hamiltonian_of_graphene(qx, qy, t=1, staggered_potential=0, valley_index=0)
+
+hamiltonian = guan.effective_hamiltonian_of_graphene_after_discretization(qx, qy, t=1, staggered_potential=0, valley_index=0)
+
 hamiltonian = guan.hamiltonian_of_graphene_with_zigzag_in_quasi_one_dimension(k, N=10, M=0, t=1, period=0)
 
 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))
diff --git a/PyPI/setup.cfg b/PyPI/setup.cfg
index b0e8575..0e6edd8 100644
--- a/PyPI/setup.cfg
+++ b/PyPI/setup.cfg
@@ -1,7 +1,7 @@
 [metadata]
 # replace with your username:
 name = guan
-version = 0.0.111
+version = 0.0.112
 author = guanjihuan
 author_email = guanjihuan@163.com
 description = An open source python package
diff --git a/PyPI/src/guan/__init__.py b/PyPI/src/guan/__init__.py
index 9ca3cfd..41cf1fa 100644
--- a/PyPI/src/guan/__init__.py
+++ b/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-0.0.111, updated on July 19, 2022.
+# The current version is guan-0.0.112, updated on July 20, 2022.
 
 # Installation: pip install --upgrade guan
 
@@ -476,6 +476,32 @@ def hamiltonian_of_graphene(k1, k2, M=0, t=1, a=1/math.sqrt(3)):
     hamiltonian = h0 + h1
     return hamiltonian
 
+def effective_hamiltonian_of_graphene(qx, qy, t=1, staggered_potential=0, valley_index=0):
+    hamiltonian = np.zeros((2, 2), dtype=complex)
+    hamiltonian[0, 0] = staggered_potential
+    hamiltonian[1, 1] = -staggered_potential
+    constant = -np.sqrt(3)/2
+    if valley_index == 0:
+        hamiltonian[0, 1] = constant*t*(qx-1j*qy)
+        hamiltonian[1, 0] = constant*t*(qx+1j*qy)
+    else:
+        hamiltonian[0, 1] = constant*t*(-qx-1j*qy)
+        hamiltonian[1, 0] = constant*t*(-qx+1j*qy)
+    return hamiltonian
+
+def effective_hamiltonian_of_graphene_after_discretization(qx, qy, t=1, staggered_potential=0, valley_index=0):
+    hamiltonian = np.zeros((2, 2), dtype=complex)
+    hamiltonian[0, 0] = staggered_potential
+    hamiltonian[1, 1] = -staggered_potential
+    constant = -np.sqrt(3)/2
+    if valley_index == 0:
+        hamiltonian[0, 1] = constant*t*(np.sin(qx)-1j*np.sin(qy))
+        hamiltonian[1, 0] = constant*t*(np.sin(qx)+1j*np.sin(qy))
+    else:
+        hamiltonian[0, 1] = constant*t*(-np.sin(qx)-1j*np.sin(qy))
+        hamiltonian[1, 0] = constant*t*(-np.sin(qx)+1j*np.sin(qy))
+    return hamiltonian
+
 def hamiltonian_of_graphene_with_zigzag_in_quasi_one_dimension(k, N=10, M=0, t=1, period=0):
     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