Update documentation :book:

abp/graphstate.py

@@ -1,4 +1,4 @@
-, "Stabilizer representation VS A&B""""
+"""
 This module implements Anders and Briegel's method for fast simulation of Clifford circuits.
 """
 
@@ -400,7 +400,8 @@ class GraphState(object):
         return state
 
     def to_stabilizer(self):
-        """ Get the stabilizer representation of the state::
+        """ 
+        Get the stabilizer representation of the state::
 
             >>> print g.to_stabilizer()
              - X I I I I
@@ -409,7 +410,7 @@ class GraphState(object):
                I I I Z I
                I I I I Z
 
-            """
+        """
         return Stabilizer(self)
 
     def __eq__(self, other):
@@ -424,10 +425,3 @@ class GraphState(object):
         g.deterministic = self.deterministic
         return g
 
-if __name__ == '__main__':
-    g = GraphState()
-    g.add_nodes(range(10))
-    g._add_edge(0, 5)
-    g.act_local_rotation(6, 10)
-    print g
-    print g.to_state_vector()

doc/index.rst

@@ -3,15 +3,19 @@
    You can adapt this file completely to your liking, but it should at least
    contain the root `toctree` directive.
 
-.. toctree::
-   :maxdepth: 2
-
 
 ``abp``
 ===============================
 
 This is the documentation for ``abp``. It's a work in progress.
 
+.. toctree::
+   :hidden:
+   :maxdepth: 2
+
+   modules
+
+
 ``abp`` is a Python port of Anders and Briegel' s `method <https://arxiv.org/abs/quant-ph/0504117>`_ for fast simulation of Clifford circuits. 
 That means that you can make quantum states of thousands of qubits, perform any sequence of Clifford operations, and measure in any of :math:`\{\sigma_x, \sigma_y, \sigma_z\}`.