From 9899d11a820f99243e9eeba554cf37d60a123158 Mon Sep 17 00:00:00 2001
From: Thomas Johnson <thomasej2000@hotmail.com>
Date: Tue, 23 May 2017 11:36:44 -0700
Subject: [PATCH 01/12] create package for component modules

---
 examples/pd_grid/pd_grid/__init__.py | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 examples/pd_grid/pd_grid/__init__.py

diff --git a/examples/pd_grid/pd_grid/__init__.py b/examples/pd_grid/pd_grid/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d

From 70b0e37b6d7d424e41a8d2b65317d5ef7cc43342 Mon Sep 17 00:00:00 2001
From: Thomas Johnson <thomasej2000@hotmail.com>
Date: Tue, 23 May 2017 11:37:09 -0700
Subject: [PATCH 02/12] add entry point for pris dilemma model

---
 examples/pd_grid/run.py | 3 +++
 1 file changed, 3 insertions(+)
 create mode 100644 examples/pd_grid/run.py

diff --git a/examples/pd_grid/run.py b/examples/pd_grid/run.py
new file mode 100644
index 00000000000..ec7d04bebfa
--- /dev/null
+++ b/examples/pd_grid/run.py
@@ -0,0 +1,3 @@
+from pd_grid.server import server
+
+server.launch()

From cfe5a2a51ebcbfafa01be0c6b7e8bcfe0f01f327 Mon Sep 17 00:00:00 2001
From: Thomas Johnson <thomasej2000@hotmail.com>
Date: Tue, 23 May 2017 12:05:55 -0700
Subject: [PATCH 03/12] rename PD_Grid outer example folder to pd_grid
 (standard)

---
 .../Demographic Prisoner's Dilemma Activation Schedule.ipynb      | 0
 examples/{PD_Grid => pd_grid}/pd_grid.py                          | 0
 examples/{PD_Grid => pd_grid}/readme.md                           | 0
 3 files changed, 0 insertions(+), 0 deletions(-)
 rename examples/{PD_Grid => pd_grid}/Demographic Prisoner's Dilemma Activation Schedule.ipynb (100%)
 rename examples/{PD_Grid => pd_grid}/pd_grid.py (100%)
 rename examples/{PD_Grid => pd_grid}/readme.md (100%)

diff --git a/examples/PD_Grid/Demographic Prisoner's Dilemma Activation Schedule.ipynb b/examples/pd_grid/Demographic Prisoner's Dilemma Activation Schedule.ipynb
similarity index 100%
rename from examples/PD_Grid/Demographic Prisoner's Dilemma Activation Schedule.ipynb
rename to examples/pd_grid/Demographic Prisoner's Dilemma Activation Schedule.ipynb
diff --git a/examples/PD_Grid/pd_grid.py b/examples/pd_grid/pd_grid.py
similarity index 100%
rename from examples/PD_Grid/pd_grid.py
rename to examples/pd_grid/pd_grid.py
diff --git a/examples/PD_Grid/readme.md b/examples/pd_grid/readme.md
similarity index 100%
rename from examples/PD_Grid/readme.md
rename to examples/pd_grid/readme.md

From 8738b87eaf1854bd24a02834b64719ac11ca542e Mon Sep 17 00:00:00 2001
From: Thomas Johnson <thomasej2000@hotmail.com>
Date: Tue, 23 May 2017 12:21:33 -0700
Subject: [PATCH 04/12] add agent portrayal in the style of conways GOLife
 portrayal

---
 examples/pd_grid/pd_grid/portrayal.py | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)
 create mode 100644 examples/pd_grid/pd_grid/portrayal.py

diff --git a/examples/pd_grid/pd_grid/portrayal.py b/examples/pd_grid/pd_grid/portrayal.py
new file mode 100644
index 00000000000..0bf8dc18d8e
--- /dev/null
+++ b/examples/pd_grid/pd_grid/portrayal.py
@@ -0,0 +1,18 @@
+def portrayPDAgent(agent):
+    '''
+    This function is registered with the visualization server to be called
+    each tick to indicate how to draw the agent in its current state.
+    :param agent:  the agent in the simulation
+    :return: the portrayal dictionary
+    '''
+    assert agent is not None
+    return {
+        "Shape": "rect",
+        "w": 1,
+        "h": 1,
+        "Filled": "true",
+        "Layer": 0,
+        "x": agent.pos[0],
+        "y": agent.pos[1],
+        "Color": "blue" if agent.isCooroperating else "red"
+    }

From dffae335fc57611b0e5b6526e4f3532407cbbda1 Mon Sep 17 00:00:00 2001
From: Thomas Johnson <thomasej2000@hotmail.com>
Date: Tue, 23 May 2017 12:23:50 -0700
Subject: [PATCH 05/12] add server in the style of conways GOLife server

---
 examples/pd_grid/pd_grid/server.py | 12 ++++++++++++
 1 file changed, 12 insertions(+)
 create mode 100644 examples/pd_grid/pd_grid/server.py

diff --git a/examples/pd_grid/pd_grid/server.py b/examples/pd_grid/pd_grid/server.py
new file mode 100644
index 00000000000..ad3dc86b598
--- /dev/null
+++ b/examples/pd_grid/pd_grid/server.py
@@ -0,0 +1,12 @@
+from mesa.visualization.ModularVisualization import ModularServer
+from mesa.visualization.modules import CanvasGrid
+
+from .portrayal import portrayPDAgent
+from .model import PDModel
+
+
+# Make a world that is 50x50, on a 500x500 display.
+canvas_element = CanvasGrid(portrayPDAgent, 50, 50, 500, 500)
+
+server = ModularServer(PDModel, [canvas_element], "Prisoner's Dilemma", 50, 50,
+                       'Random')

From 8db13496e8f0dac463f7aeb96a4f9f9add4f6892 Mon Sep 17 00:00:00 2001
From: Thomas Johnson <thomasej2000@hotmail.com>
Date: Tue, 23 May 2017 14:10:22 -0700
Subject: [PATCH 06/12] finish model (model is now seperate from agent)

---
 examples/pd_grid/pd_grid/model.py | 60 +++++++++++++++++++++++++++++++
 1 file changed, 60 insertions(+)
 create mode 100644 examples/pd_grid/pd_grid/model.py

diff --git a/examples/pd_grid/pd_grid/model.py b/examples/pd_grid/pd_grid/model.py
new file mode 100644
index 00000000000..af33531af7f
--- /dev/null
+++ b/examples/pd_grid/pd_grid/model.py
@@ -0,0 +1,60 @@
+import random
+
+from mesa import Model
+from mesa.time import BaseScheduler, RandomActivation, SimultaneousActivation
+from mesa.space import SingleGrid
+from mesa.datacollection import DataCollector
+
+from .agent import PDAgent
+
+
+class PDModel(Model):
+    ''' Model class for iterated, spatial prisoner's dilemma model. '''
+
+    schedule_types = {"Sequential": BaseScheduler,
+                      "Random": RandomActivation,
+                      "Simultaneous": SimultaneousActivation}
+
+    # This dictionary holds the payoff for this agent,
+    # keyed on: (my_move, other_move)
+
+    payoff = {("C", "C"): 1,
+              ("C", "D"): 0,
+              ("D", "C"): 1.6,
+              ("D", "D"): 0}
+
+    def __init__(self, height, width, schedule_type, payoffs=None):
+        '''
+        Create a new Spatial Prisoners' Dilemma Model.
+
+        Args:
+            height, width: Grid size. There will be one agent per grid cell.
+            schedule_type: Can be "Sequential", "Random", or "Simultaneous".
+                           Determines the agent activation regime.
+            payoffs: (optional) Dictionary of (move, neighbor_move) payoffs.
+        '''
+        self.running = True
+        self.grid = SingleGrid(height, width, torus=True)
+        self.schedule_type = schedule_type
+        self.schedule = self.schedule_types[self.schedule_type](self)
+
+        # Create agents
+        for x in range(width):
+            for y in range(height):
+                agent = PDAgent((x, y), self)
+                self.grid.place_agent(agent, (x, y))
+                self.schedule.add(agent)
+
+        self.datacollector = DataCollector({
+            "Cooperating_Agents":
+            lambda m: len([a for a in m.schedule.agents if a.move == "C"])
+        })
+
+    def step(self):
+        self.datacollector.collect(self)
+        self.schedule.step()
+
+    def run(self, n):
+        ''' Run the model for n steps. '''
+        for _ in range(n):
+            self.step()

From aa7ab471e1c33add6a9f5b68e0a80acb4223d4d4 Mon Sep 17 00:00:00 2001
From: Thomas Johnson <thomasej2000@hotmail.com>
Date: Tue, 23 May 2017 14:15:21 -0700
Subject: [PATCH 07/12] add agent model and delete no longer needed model/agent
 combined pg_grid.py

---
 examples/pd_grid/pd_grid.py       | 116 ------------------------------
 examples/pd_grid/pd_grid/agent.py |  52 ++++++++++++++
 2 files changed, 52 insertions(+), 116 deletions(-)
 delete mode 100644 examples/pd_grid/pd_grid.py
 create mode 100644 examples/pd_grid/pd_grid/agent.py

diff --git a/examples/pd_grid/pd_grid.py b/examples/pd_grid/pd_grid.py
deleted file mode 100644
index 4e73357eaa8..00000000000
--- a/examples/pd_grid/pd_grid.py
+++ /dev/null
@@ -1,116 +0,0 @@
-'''
-Spatial Demographic Prisoner's Dilemma
-=========================================
-
-In this model, agents are situated on a grid and play the Prisoner's Dilemma
-with all of their neighbors simultaneously; an agent can either be Cooperating
-or Defecting.
-'''
-
-import random
-
-from mesa import Agent, Model
-from mesa.datacollection import DataCollector
-from mesa.time import BaseScheduler, RandomActivation, SimultaneousActivation
-from mesa.space import SingleGrid
-
-
-class PD_Agent(Agent):
-
-    def __init__(self, pos, model, starting_move=None):
-        '''
-        Create a new Prisoner's Dilemma agent.
-
-        Args:
-            pos: (x, y) tuple of the agent's position.
-            starting_move: If provided, determines the agent's initial state:
-                           C(ooperating) or D(efecting). Otherwise, random.
-        '''
-        super().__init__(pos, model)
-        self.pos = pos
-        self.score = 0
-        if starting_move:
-            self.move = starting_move
-        else:
-            self.move = random.choice(["C", "D"])
-        self.next_move = None
-
-    def step(self):
-        '''
-        Get the neighbors' moves, and change own move accordingly.
-        '''
-        neighbors = self.model.grid.get_neighbors(self.pos, True,
-                                                  include_center=True)
-        best_neighbor = max(neighbors, key=lambda a: a.score)
-        self.next_move = best_neighbor.move
-
-        if self.model.schedule_type != "Simultaneous":
-            self.advance()
-
-    def advance(self):
-        self.move = self.next_move
-        self.score += self.increment_score()
-
-    def increment_score(self):
-        neighbors = self.model.grid.get_neighbors(self.pos, True)
-        if self.model.schedule_type == "Simultaneous":
-            moves = [neighbor.next_move for neighbor in neighbors]
-        else:
-            moves = [neighbor.move for neighbor in neighbors]
-        return sum(self.model.payoff[(self.move, move)] for move in moves)
-
-
-class PD_Model(Model):
-    '''
-    Model class for iterated, spatial prisoner's dilemma model.
-    '''
-
-    schedule_types = {"Sequential": BaseScheduler,
-                      "Random": RandomActivation,
-                      "Simultaneous": SimultaneousActivation}
-
-    # This dictionary holds the payoff for this agent,
-    # keyed on: (my_move, other_move)
-
-    payoff = {("C", "C"): 1,
-              ("C", "D"): 0,
-              ("D", "C"): 1.6,
-              ("D", "D"): 0}
-
-    def __init__(self, height, width, schedule_type, payoffs=None):
-        '''
-        Create a new Spatial Prisoners' Dilemma Model.
-
-        Args:
-            height, width: Grid size. There will be one agent per grid cell.
-            schedule_type: Can be "Sequential", "Random", or "Simultaneous".
-                           Determines the agent activation regime.
-            payoffs: (optional) Dictionary of (move, neighbor_move) payoffs.
-        '''
-        self.running = True
-        self.grid = SingleGrid(height, width, torus=True)
-        self.schedule_type = schedule_type
-        self.schedule = self.schedule_types[self.schedule_type](self)
-
-        # Create agents
-        for x in range(width):
-            for y in range(height):
-                agent = PD_Agent((x, y), self)
-                self.grid.place_agent(agent, (x, y))
-                self.schedule.add(agent)
-
-        self.datacollector = DataCollector({
-            "Cooperating_Agents":
-            lambda m: len([a for a in m.schedule.agents if a.move == "C"])
-        })
-
-    def step(self):
-        self.datacollector.collect(self)
-        self.schedule.step()
-
-    def run(self, n):
-        '''
-        Run the model for a certain number of steps.
-        '''
-        for _ in range(n):
-            self.step()
diff --git a/examples/pd_grid/pd_grid/agent.py b/examples/pd_grid/pd_grid/agent.py
new file mode 100644
index 00000000000..b81ced5220c
--- /dev/null
+++ b/examples/pd_grid/pd_grid/agent.py
@@ -0,0 +1,52 @@
+import random
+
+from mesa import Agent
+
+
+class PDAgent(Agent):
+    ''' Agent member of the iterated, spatial prisoner's dilemma model. '''
+
+    def __init__(self, pos, model, starting_move=None):
+        '''
+        Create a new Prisoner's Dilemma agent.
+
+        Args:
+            pos: (x, y) tuple of the agent's position.
+            model: model instance
+            starting_move: If provided, determines the agent's initial state:
+                           C(ooperating) or D(efecting). Otherwise, random.
+        '''
+        super().__init__(pos, model)
+        self.pos = pos
+        self.score = 0
+        if starting_move:
+            self.move = starting_move
+        else:
+            self.move = random.choice(["C", "D"])
+        self.next_move = None
+
+    @property
+    def isCooroperating(self):
+        return self.move == "C"
+
+    def step(self):
+        ''' Get the neighbors' moves, and change own move accordingly. '''
+        neighbors = self.model.grid.get_neighbors(self.pos, True,
+                                                  include_center=True)
+        best_neighbor = max(neighbors, key=lambda a: a.score)
+        self.next_move = best_neighbor.move
+
+        if self.model.schedule_type != "Simultaneous":
+            self.advance()
+
+    def advance(self):
+        self.move = self.next_move
+        self.score += self.increment_score()
+
+    def increment_score(self):
+        neighbors = self.model.grid.get_neighbors(self.pos, True)
+        if self.model.schedule_type == "Simultaneous":
+            moves = [neighbor.next_move for neighbor in neighbors]
+        else:
+            moves = [neighbor.move for neighbor in neighbors]
+        return sum(self.model.payoff[(self.move, move)] for move in moves)

From fa5b6b419eea7be742348b49f110bcc42b6ddcce Mon Sep 17 00:00:00 2001
From: Thomas Johnson <thomasej2000@hotmail.com>
Date: Tue, 23 May 2017 14:20:10 -0700
Subject: [PATCH 08/12] make jupyter notebook example work with new model
 standard format (warning: jupyter notebook example changes result in a lot of
 lines of code)

---
 ...isoner's Dilemma Activation Schedule.ipynb | 40 ++++++++-----------
 1 file changed, 16 insertions(+), 24 deletions(-)

diff --git a/examples/pd_grid/Demographic Prisoner's Dilemma Activation Schedule.ipynb b/examples/pd_grid/Demographic Prisoner's Dilemma Activation Schedule.ipynb
index 9fd422d93bc..e973056a625 100644
--- a/examples/pd_grid/Demographic Prisoner's Dilemma Activation Schedule.ipynb	
+++ b/examples/pd_grid/Demographic Prisoner's Dilemma Activation Schedule.ipynb	
@@ -31,12 +31,10 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "from pd_grid import PD_Model\n",
+    "from pd_grid.model import PDModel\n",
     "\n",
     "import random\n",
     "import numpy as np\n",
@@ -132,15 +130,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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1KQ488EDmzp07fPzTTz+dJ598kvvvv58vfelLwwXhMcccw+c+9znuuOMOAJ56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\nAAAASUVORK5CYII=\n",
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LkiRJBRikJUmSpAIM0pIkSVIBLVUjXbb2Nk8dctm63b7K1kzX2mD7q3aP62Z6bVDd2vlG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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x109782780>"
+       "<matplotlib.figure.Figure at 0x107639898>"
       ]
      },
      "metadata": {},
@@ -149,7 +145,7 @@
    ],
    "source": [
     "random.seed(seed)\n",
-    "m = PD_Model(50, 50, \"Sequential\")\n",
+    "m = PDModel(50, 50, \"Sequential\")\n",
     "run_model(m)"
    ]
   },
@@ -163,15 +159,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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aNX8Hq9McaZakcSaCJYHPA/8GvDeTC1vPS5IWw0yzJI0jEawJnAHM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\np1k0N6Q11+0uwFERbNF0ezT2RPDiCL4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61v3CkRyi/RqLdJ+Kdx96AGgsOCINAAAABEAhDQAAAARAIQ0AAAAEkPAZ6ZrEsh9wXbYf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\niNTD/wO/BTXv45mRqwAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x109c4dc88>"
+       "<matplotlib.figure.Figure at 0x1078b8b00>"
       ]
      },
      "metadata": {},
@@ -180,7 +174,7 @@
    ],
    "source": [
     "random.seed(seed)\n",
-    "m = PD_Model(50, 50, \"Random\")\n",
+    "m = PDModel(50, 50, \"Random\")\n",
     "run_model(m)"
    ]
   },
@@ -196,15 +190,13 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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OONWdG66SDxv2fe/LKp+hNn+mpxvmOLCOOauJaFOuVxoUzzRLkiRJBSyaJUmSpAIWzZIk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\n0SBU1Z3APyZ8PNVwKqlXpshf8BqsMetL0Xww8Mh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LmD59Os888wwf/OAHKSkpYejQoVxwQVPu6LXXXuOVV15h7ty5QFM0ZdiwYScc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\nFhEREREZh/8Db2EkkUv9nuUAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10a206550>"
+       "<matplotlib.figure.Figure at 0x107803198>"
       ]
      },
      "metadata": {},
@@ -213,7 +205,7 @@
    ],
    "source": [
     "random.seed(seed)\n",
-    "m = PD_Model(50, 50, \"Simultaneous\")\n",
+    "m = PDModel(50, 50, \"Simultaneous\")\n",
     "run_model(m)"
    ]
   }
@@ -234,9 +226,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.2"
+   "version": "3.6.0"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }

From b7ae65c16e2a393de3efa1af1d4d257f543b0c74 Mon Sep 17 00:00:00 2001
From: Thomas Johnson <thomasej2000@hotmail.com>
Date: Tue, 23 May 2017 14:33:07 -0700
Subject: [PATCH 09/12] update readme to reflect new model organization

---
 examples/pd_grid/readme.md | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/examples/pd_grid/readme.md b/examples/pd_grid/readme.md
index cfbed3e819a..b80e32b9d91 100644
--- a/examples/pd_grid/readme.md
+++ b/examples/pd_grid/readme.md
@@ -2,7 +2,7 @@
 
 ## Summary
 
-The Demographic Prisoner's Dilemma is a family of variants on the classic two-player [Prisoner's Dilemma]. The model consists of agents, each with a strategy of either Cooperate or Defect. Each agent's payoff is based on its strategy and the strategies of its spatial neighbors. After each step of the model, the agents adopt the strategy of their neighbor with the highest total score. 
+The Demographic Prisoner's Dilemma is a family of variants on the classic two-player [Prisoner's Dilemma]. The model consists of agents, each with a strategy of either Cooperate or Defect. Each agent's payoff is based on its strategy and the strategies of its spatial neighbors. After each step of the model, the agents adopt the strategy of their neighbor with the highest total score.
 
 The model payoff table is:
 
@@ -13,15 +13,20 @@ The model payoff table is:
 
 Where *D* is the defection bonus, generally set higher than 1. In these runs, the defection bonus is set to $D=1.6$.
 
-The Demographic Prisoner's Dilemma demonstrates how simple rules can lead to the emergence of widespread cooperation, despite the Defection strategy dominiating each individual interaction game. However, it is also interesting for another reason: it is known to be sensitive to the activation regime employed in it.
+The Demographic Prisoner's Dilemma demonstrates how simple rules can lead to the emergence of widespread cooperation, despite the Defection strategy dominating each individual interaction game. However, it is also interesting for another reason: it is known to be sensitive to the activation regime employed in it.
 
 ## How to Run
 
+* Web based model simulation
+Run ``python run.py``.
+
+* Jupyter Notebook
 Launch the ``Demographic Prisoner's Dilemma Activation Schedule.ipynb`` notebook and run the code.
 
 ## Files
 
-* ``pd_grid.py``: has the model and agent classes; the model takes a schedule_type string as an argument, which determines what schedule type the model uses: Sequential, Random or Simultaneous. 
+* ``run.py`` is the entry point for the font-end simulations.
+* ``pd_grid/``: contains the model and agent classes; the model takes a ``schedule_type`` string as an argument, which determines what schedule type the model uses: Sequential, Random or Simultaneous.
 * ``Demographic Prisoner's Dilemma Activation Schedule.ipynb``: Jupyter Notebook for running the scheduling experiment. This runs the model three times, one for each activation type, and demonstrates how the activation regime drives the model to different outcomes.
 
 ## Further Reading

From e70a915dbe8974aba83270f5829c70f895c96701 Mon Sep 17 00:00:00 2001
From: Thomas Johnson <thomasej2000@hotmail.com>
Date: Tue, 23 May 2017 14:37:52 -0700
Subject: [PATCH 10/12] small styling change in readme

---
 examples/pd_grid/readme.md | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/examples/pd_grid/readme.md b/examples/pd_grid/readme.md
index b80e32b9d91..d82567b5539 100644
--- a/examples/pd_grid/readme.md
+++ b/examples/pd_grid/readme.md
@@ -17,10 +17,12 @@ The Demographic Prisoner's Dilemma demonstrates how simple rules can lead to the
 
 ## How to Run
 
-* Web based model simulation
+##### Web based model simulation
+
 Run ``python run.py``.
 
-* Jupyter Notebook
+##### Jupyter Notebook
+
 Launch the ``Demographic Prisoner's Dilemma Activation Schedule.ipynb`` notebook and run the code.
 
 ## Files

From 300c2f5efe84f9d9bd56a5e50a0db883917198df Mon Sep 17 00:00:00 2001
From: Thomas Johnson <thomasej2000@hotmail.com>
Date: Tue, 23 May 2017 14:55:08 -0700
Subject: [PATCH 11/12] no need to import random in model

---
 examples/pd_grid/pd_grid/model.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/examples/pd_grid/pd_grid/model.py b/examples/pd_grid/pd_grid/model.py
index af33531af7f..421e28c8b3b 100644
--- a/examples/pd_grid/pd_grid/model.py
+++ b/examples/pd_grid/pd_grid/model.py
@@ -1,5 +1,3 @@
-import random
-
 from mesa import Model
 from mesa.time import BaseScheduler, RandomActivation, SimultaneousActivation
 from mesa.space import SingleGrid

From e069c08a9366a9395803759a1a6d6f1a571aabc1 Mon Sep 17 00:00:00 2001
From: Thomas Johnson <thomasej2000@hotmail.com>
Date: Tue, 23 May 2017 15:38:41 -0700
Subject: [PATCH 12/12] rename notebook to standard analysis.ipynb

---
 ...risoner's Dilemma Activation Schedule.ipynb => analysis.ipynb} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename examples/pd_grid/{Demographic Prisoner's Dilemma Activation Schedule.ipynb => analysis.ipynb} (100%)

diff --git a/examples/pd_grid/Demographic Prisoner's Dilemma Activation Schedule.ipynb b/examples/pd_grid/analysis.ipynb
similarity index 100%
rename from examples/pd_grid/Demographic Prisoner's Dilemma Activation Schedule.ipynb
rename to examples/pd_grid/analysis.ipynb