diff --git a/alias/classes/argument.py b/alias/classes/argument.py
index 8c00c2b..18524cc 100644
--- a/alias/classes/argument.py
+++ b/alias/classes/argument.py
@@ -7,6 +7,14 @@ def __init__(self, name, mapping=0, clause_mapping=0):
         self.mapping = mapping
         self.clause_mapping = clause_mapping
 
+    def __str__(self):
+        string = 'Argument \'' + self.name + '\' : Attacking: ['
+        string = string + (', '.join(self.attacking))
+        string = string + '], Attacked by: ['
+        string = string + (', '.join(self.attacked_by))
+        string = string + ']'
+        return string
+
     def add_attack(self, attacked):
         self.attacking.append(attacked)
 
diff --git a/alias/classes/argumentationframework.py b/alias/classes/argumentationframework.py
index aab3fac..2675c58 100644
--- a/alias/classes/argumentationframework.py
+++ b/alias/classes/argumentationframework.py
@@ -44,6 +44,15 @@ def __str__(self):
             my_string += '|\n'
         return my_string
 
+    def __contains__(self, arg):
+        return arg in self.arguments
+
+    def __iter__(self):
+        return iter(self.arguments)
+
+    def __getitem__(self, arg):
+        return self.arguments[arg]
+
     def add_argument(self, arg):
         """
         Method to add argument to argumentation framework
@@ -80,6 +89,11 @@ def add_attack(self, attack):
         self.arguments[attacked].attacked_by.append(attacker)
         self.__solverManager.dirty = True
 
+    def get_attackers(self, argument):
+        assert self.__contains__(argument)
+        argument: Argument = self.arguments[argument]
+        return argument.attacked_by
+
     def __get_graph(self):
         graph = nx.DiGraph()
         for n in self.arguments.keys():
@@ -105,7 +119,7 @@ def get_stable_extension(self):
 
     def get_some_stable_extension(self):
         return self.__solverManager.get_some_extension(ExtensionType.STABLE, self.arguments, self.attacks, self.matrix)
-    
+
     def get_complete_extension(self):
         return self.__solverManager.get_extension(ExtensionType.COMPLETE, self.arguments, self.attacks, self.matrix)
 
diff --git a/examples/demonstration-notebook.ipynb b/examples/demonstration-notebook.ipynb
index 605f362..6cd83dc 100644
--- a/examples/demonstration-notebook.ipynb
+++ b/examples/demonstration-notebook.ipynb
@@ -1,5 +1,19 @@
 {
  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import local module to track changes\n",
+    "import os\n",
+    "import sys\n",
+    "module_path = os.path.abspath(os.path.join('..'))\n",
+    "if module_path not in sys.path:\n",
+    "    sys.path.append(module_path)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -17,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
@@ -36,7 +50,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
@@ -58,7 +72,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
@@ -80,21 +94,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ArgumentationFramework 'Example' : {'a' : ['b'], 'c' : [], 'b' : ['c']}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "print example"
+    "print(example)"
    ]
   },
   {
@@ -106,217 +112,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "set(['a'])\n",
-      "Argument 'a' : ['b']\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "arga = example['a']\n",
-    "print example.get_attackers('b')\n",
-    "print arga"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "ALIAS utilises NetworkX's drawing library to provide visual representations of Argumentation Frameworks.  Lets draw our AF 'example' using the `draw_framework()` function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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kD9HKf3JTE6tWrerusERE4rT5sHUzWwsku+fw7pYr7u5m9rFy3czOB/43MA6o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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11058b810>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Creating drawings requires \n",
-    "# matplotlib and NetworkX (NetworkX is imported by ALIAS internally)\n",
-    "%matplotlib inline\n",
-    "\n",
-    "import matplotlib\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Call ALIAS' drawing facility on our AF\n",
-    "al.draw_framework(example)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Lets apply a labelling to our AF.  We can use ALIAS' built-in `generate_all_in()` function to generate an \"All-In labelling\" for our framework."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Labelling 'All-In' : {in : ['a', 'c', 'b'] out : [] undecided : [] undefined : []}\n",
-      "Labelling 'All-In' : {in : ['a', 'c', 'b'] out : [] undecided : [] undefined : ['d']}\n",
-      "Labelling 'All-In' : {in : ['a', 'c', 'b'] out : [] undecided : [] undefined : []}\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Call the generate_all_in() function on the example AF\n",
-    "allin = example.generate_all_in()\n",
-    "print allin\n",
-    "# Labellings are updated dynamically when the AF is updated\n",
-    "example.add_argument('d')\n",
-    "print allin\n",
-    "example.remove_argument('d')\n",
-    "print allin"
+    "print(example.get_attackers('b'))\n",
+    "print(arga)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "`draw_framework()` can also take a labelling as an argument to colour code the arguments in the representation:"
+    "ALIAS utilises NetworkX's drawing library to provide visual representations of Argumentation Frameworks.  Lets draw our AF 'example' using the `draw_graph()` function:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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ZfdXM3jOzU2bW6rFgZnaVme0ys91m9qOuzk9ERLpHKHv879Jw9e/81gaYWSLw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\nSUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11075c610>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "al.draw_framework(example, labelling=allin)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Lets apply some semantics to our AF.  ALIAS can handle both extension-based and labelling-based semantics."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[<alias.classes.labelling.Labelling object at 0x1107b9f50>]\n"
-     ]
-    }
-   ],
-   "source": [
-    "preflab = al.labelling_preferred(example)\n",
-    "print preflab"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Labelling semantic functions return a list of all labellings for the given framework that satisfy the constraints of the chosen semantics."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Labelling 'All-In' : {in : ['a', 'c'] out : ['b'] undecided : [] undefined : []}\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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IeQ/gGgP0MLPtQLfgOmbW2MwWFvCd2M3shUhOTubpF54mc1AmtKTwW+DxkNU9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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1107c4050>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "print preflab[0]\n",
-    "al.draw_framework(example, labelling=preflab[0])"
+    "example.draw_graph()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Lets try some extension based semantics.  These functions take the power set of all arguments in the framework and applys filtering to find the chosen semantics.  (Labellings can also be converted to extension using the function `labelling.lab2ext()`)"
+    "Lets try some extension based semantics.  These functions generate SAT encoding for specified extension and produce the output using SAT solver."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[frozenset(['a', 'c'])]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "stabext = example.extension_stable()\n",
-    "print stabext"
+    "print('Stable: ', example.get_stable_extension())\n",
+    "print('Complete: ', example.get_complete_extension())\n",
+    "print('Preferred: ', example.get_preferred_extension())"
    ]
   },
   {
@@ -328,215 +170,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ArgumentationFramework 'example-apx.apx' : {'a' : ['b'], 'c' : ['b'], 'b' : [], 'd' : ['c']}\n"
-     ]
-    }
-   ],
-   "source": [
-    "exampleapx = al.read_apx('example-apx.apx')\n",
-    "print exampleapx"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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K4t6bbuKdoiI61/A7O4BBLVowbeFCrrv++vosL67Upcev4BeRiCopKaFLu3as/PZb+tby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\nRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1107cd3d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "al.draw_framework(exampleapx)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "grounded = al.labelling_grounded(exampleapx)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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3l/pByufi76XxYGIijbp35+VZs9RFNAJUdOKvA7wONAM2Av2dc1lm1giY5Jy7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\nDVCkTaYnAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1109dd810>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "al.draw_framework(exampleapx, labelling=grounded)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "pref = al.labelling_preferred(exampleapx)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[<alias.classes.labelling.Labelling object at 0x110c68a90>]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print pref"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Labelling 'All-In' : {in : ['a', 'd'] out : ['c', 'b'] undecided : [] undefined : []}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print pref[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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044jPExNpf8IJ4S5NRCJMVw0JAHl5eZxy/PF8W1REs0rTjgOaAW8DKcAgIJNDzxMUAb9M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\nYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x110c71950>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "al.draw_framework(exampleapx, labelling=pref[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "al.to_neo4j(af=exampleapx, u='neo4j', p='test')"
+    "exampleapx = al.read_apx('example-apx.apx')\n",
+    "print(exampleapx)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "f = al.from_neo4j(framework='exampleapx', u='neo4j', p='test')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ArgumentationFramework 'exampleapx' : {}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print f"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "UnmappedInstanceError",
-     "evalue": "Class 'alias.classes.framework.ArgumentationFramework' is mapped, but this instance lacks instrumentation.  This occurs when the instanceis created before sqlalchemy.orm.mapper(alias.classes.framework.ArgumentationFramework) was called.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mUnmappedInstanceError\u001b[0m                     Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-14-353240770876>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mdb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mal\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDbwrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_sqlite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexampleapx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m/usr/local/Cellar/python/2.7.9/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/ALIAS-0.1-py2.7.egg/alias/inout/db/dbwrapper.pyc\u001b[0m in \u001b[0;36mto_sqlite\u001b[0;34m(self, af)\u001b[0m\n\u001b[1;32m     55\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m         \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDBSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m         \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     58\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m             \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/sqlalchemy/orm/session.pyc\u001b[0m in \u001b[0;36madd\u001b[0;34m(self, instance, _warn)\u001b[0m\n\u001b[1;32m   1573\u001b[0m             \u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mattributes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minstance_state\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1574\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNO_STATE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1575\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mUnmappedInstanceError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1576\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1577\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_save_or_update_state\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mUnmappedInstanceError\u001b[0m: Class 'alias.classes.framework.ArgumentationFramework' is mapped, but this instance lacks instrumentation.  This occurs when the instanceis created before sqlalchemy.orm.mapper(alias.classes.framework.ArgumentationFramework) was called."
-     ]
-    }
-   ],
-   "source": [
-    "db = al.Dbwrapper()\n",
-    "db.to_sqlite(exampleapx)"
+    "exampleapx.draw_graph()"
    ]
   },
   {
@@ -546,26 +198,33 @@
     "collapsed": true
    },
    "outputs": [],
-   "source": []
+   "source": [
+    "print('Stable: ', exampleapx.get_stable_extension())\n",
+    "print('Complete: ', exampleapx.get_complete_extension())\n",
+    "print('Preferred: ', exampleapx.get_preferred_extension())"
+   ]
   }
  ],
  "metadata": {
+  "interpreter": {
+   "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
+  },
   "kernelspec": {
    "display_name": "Python 2",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.9"
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,