Repository: projectmesa/mesa
Pull request #288 (2016-07-17 16:57:02)

Refactor forest fire example #223


Patch:
diff --git a/examples/Basic/Readme.md b/examples/Basic/Readme.md
deleted file mode 100644
index d4ac4b96..00000000
--- a/examples/Basic/Readme.md
+++ /dev/null
@@ -1,41 +0,0 @@
-# Basic Grid with two agents
-
-## Summary
-
-A very basic example model to showcase the visulaization on web browser.
-
-A simple grid is dispalyed on browesr with two agents. The example does not
-have any agent motion involved. This example does not have any movenment of
-agents so as to keep it to the simplest of level possible.
-
-This model showcases following features:
-
-* A rectangular grid
-* Text Overlay on the agent's shape on CanvasGrid
-* ArrowHead shaped agent for displaying heading of the agent on CanvasGrid
-
-## Installation
-
-To install the dependencies use pip and the requirements.txt in this directory.
-e.g.
-
-```
-    $ pip install -r requirements.txt
-```
-
-## How to Run
-
-To run the model interactively, run ``run.py`` in this directory. e.g.
-
-```
-    $ python shapes_viz.py
-```
-
-Then open your browser to [http://127.0.0.1:8888/](http://127.0.0.1:8888/) and
-press Reset, then Run.
-
-## Files
-
-* ``basic/model.py: Defines the Basic model and agents.
-* ``basic/server.py``: Sets up the interactive visualization server.
-* ``run.py``: Launches a model visualization server.
diff --git a/examples/Basic/basic/model.py b/examples/Basic/basic/model.py
deleted file mode 100644
index db832772..00000000
--- a/examples/Basic/basic/model.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import random
-
-from mesa import Model, Agent
-from mesa.space import SingleGrid
-from mesa.time import RandomActivation
-
-
-class Walker(Agent):
-    def __init__(self, unique_id, pos, heading=(1, 0)):
-        self.unique_id = unique_id
-        self.pos = pos
-        self.heading = heading
-        self.headings = {(1, 0), (0, 1), (-1, 0), (0, -1)}
-
-
-class ShapesModel(Model):
-    def __init__(self, N, width=20, height=10):
-        self.running = True
-        self.N = N    # num of agents
-        self.headings = ((1, 0), (0, 1), (-1, 0), (0, -1))  # tuples are fast
-        self.grid = SingleGrid(width, height, torus=False)
-        self.schedule = RandomActivation(self)
-        self.make_walker_agents()
-
-    def make_walker_agents(self):
-        unique_id = 0
-        while True:
-            if unique_id == self.N:
-                break
-            x = random.randrange(self.grid.width)
-            y = random.randrange(self.grid.height)
-            pos = (x, y)
-            heading = random.choice(self.headings)
-            # heading = (1, 0)
-            if self.grid.is_cell_empty(pos):
-                print("Creating agent {2} at ({0}, {1})"
-                      .format(x, y, unique_id))
-                a = Walker(unique_id, pos, heading)
-                self.schedule.add(a)
-                self.grid.place_agent(a, pos)
-                unique_id += 1
-
-    def step(self):
-        self.schedule.step()
diff --git a/examples/Basic/basic/server.py b/examples/Basic/basic/server.py
deleted file mode 100644
index 11df9f62..00000000
--- a/examples/Basic/basic/server.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import random
-
-from basic.model import Walker, ShapesModel
-from mesa.visualization.modules import CanvasGrid
-from mesa.visualization.ModularVisualization import ModularServer
-
-
-def agent_draw(agent):
-    portrayal = None
-    if agent is None:
-        # Actually this if part is unnecessary, but still keeping it for
-        # aesthetics
-        pass
-    elif isinstance(agent, Walker):
-        print("Uid: {0}, Heading: {1}".format(agent.unique_id, agent.heading))
-        portrayal = {"Shape": "arrowHead",
-                     "Filled": "true",
-                     "Layer": 2,
-                     "Color": "green",
-                     "Filled": "true",
-                     "heading0": agent.heading[0],
-                     "heading1": agent.heading[1],
-                     "text": agent.unique_id,
-                     "text_color": "white",
-                     "scale": 0.8,
-                     }
-    return portrayal
-
-
-def launch_basic():
-    width = 15
-    height = 10
-    num_agents = 2
-    pixel_ratio = 50
-    grid = CanvasGrid(agent_draw, width, height,
-                      width*pixel_ratio, height*pixel_ratio)
-    server = ModularServer(ShapesModel, [grid], "Basic Example",
-                           num_agents, width, height)
-    server.max_steps = 0
-    server.port = 8888
-    server.launch()
-
-if __name__ == "__main__":
-    random.seed(3)
-    launch_basic()
diff --git a/examples/Basic/requirements.txt b/examples/Basic/requirements.txt
deleted file mode 100644
index da0b5b95..00000000
--- a/examples/Basic/requirements.txt
+++ /dev/null
@@ -1,1 +0,0 @@
-mesa
diff --git a/examples/Basic/run.py b/examples/Basic/run.py
deleted file mode 100644
index 23edc675..00000000
--- a/examples/Basic/run.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from basic.server import launch_basic
-
-launch_basic()
diff --git a/examples/ForestFire/Forest Fire Model.ipynb b/examples/ForestFire/Forest Fire Model.ipynb
index cee15e47..dcefadf4 100644
--- a/examples/ForestFire/Forest Fire Model.ipynb
+++ b/examples/ForestFire/Forest Fire Model.ipynb
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
@@ -40,7 +40,7 @@
     "from mesa.time import RandomActivation\n",
     "from mesa.space import Grid\n",
     "from mesa.datacollection import DataCollector\n",
-    "from mesa.batchrunner import BatchRunner "
+    "from mesa.batchrunner import BatchRunner"
    ]
   },
   {
@@ -58,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false
    },
@@ -95,8 +95,7 @@
     "            for neighbor in neighbors:\n",
     "                if neighbor.condition == \"Fine\":\n",
     "                    neighbor.condition = \"On Fire\"\n",
-    "            self.condition = \"Burned Out\"\n",
-    "        "
+    "            self.condition = \"Burned Out\""
    ]
   },
   {
@@ -116,7 +115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
@@ -192,7 +191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
@@ -210,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
@@ -234,7 +233,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -252,7 +251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
@@ -260,18 +259,18 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x109936390>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1121d3e10>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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GCcUhUE1eRETqUE1eRKTAqSYvIlKilOQzSPXHQHFQDBKKQ6CavIiI1KGavIhI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\niBQxJXkRkSKmJC8iUsSU5EV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3TZVSnkTb6D1MSYntq56RUbPs2wdZWXDwoG2KGTDAXkgdPdq2u3fp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\n9/8BgdQMf4sV9M8AAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10992dc18>"
+       "<matplotlib.figure.Figure at 0x1121d3630>"
       ]
      },
      "metadata": {},
@@ -293,7 +292,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false
    },
@@ -301,18 +300,18 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x109bee4e0>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x112ea62b0>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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D0jRJQ/Pat5H0erLvmqa/PcepHYvsSWBL4FvAFMXapDHnSfQAHgbWAwZlzcBX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\nrHrT5gzMQmA+8EnyOh94g3CjdGTFTdQ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LoAaDeKnqS7Y15SxcCM8/b03z16iRLSHcNa3Ruz9tulEea/ux7bSJbEO5guX4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\n4rWpNroaa/atsTukLOe2Nfpff4WXX4a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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x109beaa90>"
+       "<matplotlib.figure.Figure at 0x112ea19e8>"
       ]
      },
      "metadata": {},
@@ -341,7 +340,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false
    },
@@ -350,12 +349,12 @@
     "param_set = dict(height=50, # Height and width are constant\n",
     "                 width=50,\n",
     "                 # Vary density from 0.01 to 1, in 0.01 increments:\n",
-    "                 density=np.linspace(0,1,101)[1:]) "
+    "                 density=np.linspace(0,1,101)[1:])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false
    },
@@ -368,7 +367,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
@@ -387,7 +386,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
@@ -405,7 +404,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false
    },
@@ -416,7 +415,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
@@ -424,7 +423,7 @@
     {
      "data": {
       "text/html": [
-       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<div>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -439,43 +438,43 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td> 0.022152</td>\n",
-       "      <td> 11</td>\n",
-       "      <td> 0.12</td>\n",
-       "      <td> 50</td>\n",
-       "      <td> 50</td>\n",
+       "      <td>0.043771</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0.12</td>\n",
+       "      <td>50</td>\n",
+       "      <td>50</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td> 1.000000</td>\n",
-       "      <td> 98</td>\n",
-       "      <td> 0.99</td>\n",
-       "      <td> 50</td>\n",
-       "      <td> 50</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>98</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>50</td>\n",
+       "      <td>50</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td> 0.066225</td>\n",
-       "      <td> 48</td>\n",
-       "      <td> 0.49</td>\n",
-       "      <td> 50</td>\n",
-       "      <td> 50</td>\n",
+       "      <td>0.087948</td>\n",
+       "      <td>48</td>\n",
+       "      <td>0.49</td>\n",
+       "      <td>50</td>\n",
+       "      <td>50</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td> 0.000000</td>\n",
-       "      <td>  3</td>\n",
-       "      <td> 0.04</td>\n",
-       "      <td> 50</td>\n",
-       "      <td> 50</td>\n",
+       "      <td>0.010309</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>50</td>\n",
+       "      <td>50</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td> 0.033095</td>\n",
-       "      <td> 42</td>\n",
-       "      <td> 0.43</td>\n",
-       "      <td> 50</td>\n",
-       "      <td> 50</td>\n",
+       "      <td>0.036313</td>\n",
+       "      <td>42</td>\n",
+       "      <td>0.43</td>\n",
+       "      <td>50</td>\n",
+       "      <td>50</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -483,14 +482,14 @@
       ],
       "text/plain": [
        "   BurnedOut  Run  density  height  width\n",
-       "0   0.022152   11     0.12      50     50\n",
+       "0   0.043771   11     0.12      50     50\n",
        "1   1.000000   98     0.99      50     50\n",
-       "2   0.066225   48     0.49      50     50\n",
-       "3   0.000000    3     0.04      50     50\n",
-       "4   0.033095   42     0.43      50     50"
+       "2   0.087948   48     0.49      50     50\n",
+       "3   0.010309    3     0.04      50     50\n",
+       "4   0.036313   42     0.43      50     50"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -508,7 +507,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
@@ -519,15 +518,15 @@
        "(0, 1)"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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+      "image/png": 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/Bu4GdgYLuPv78u/N7FvAd8ICQKQdpVIpLl1KA6eA7JnA3Nw0qVSqofUSgQiuCbj7O8Bu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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10a10c550>"
+       "<matplotlib.figure.Figure at 0x1120ccfd0>"
       ]
      },
      "metadata": {},
@@ -552,32 +551,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0, 1)"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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9JMJVf9KzwF53PwQcMrN/Ad4BVAR+d9+Qsy0iHa3Z7p0a9NU54gXxUGndzL7QzHHyBv4H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\nAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10a42b9b0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "param_run = BatchRunner(ForestFire, param_set, iterations=5, model_reporters=model_reporter)\n",
     "param_run.run_all()\n",
@@ -588,24 +566,25 @@
   }
  ],
  "metadata": {
+  "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python [se]",
    "language": "python",
-   "name": "python3"
+   "name": "Python [se]"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 3
+    "version": 3.0
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.2"
+   "version": "3.5.2"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
+}
\ No newline at end of file
diff --git a/examples/ForestFire/forest_fire/__init__.py b/examples/ForestFire/forest_fire/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/examples/ForestFire/forest_fire/agent.py b/examples/ForestFire/forest_fire/agent.py
new file mode 100644
index 00000000..e78fa25c
--- /dev/null
+++ b/examples/ForestFire/forest_fire/agent.py
@@ -0,0 +1,37 @@
+from mesa import Agent
+
+
+class TreeCell(Agent):
+    """
+    A tree cell.
+
+    Attributes:
+        x, y: Grid coordinates
+        condition: Can be "Fine", "On Fire", or "Burned Out"
+        unique_id: (x,y) tuple.
+
+    unique_id isn't strictly necessary here, but it's good
+    practice to give one to each agent anyway.
+    """
+    def __init__(self, pos):
+        """
+        Create a new tree.
+        Args:
+            pos: The tree's coordinates on the grid.
+        """
+        self.pos = pos
+        self.unique_id = pos
+        self.condition = "Fine"
+
+    def step(self, model):
+        """
+        If the tree is on fire, spread it to fine trees nearby.
+        """
+        if self.condition == "On Fire":
+            for neighbor in model.grid.neighbor_iter(self.pos):
+                if neighbor.condition == "Fine":
+                    neighbor.condition = "On Fire"
+            self.condition = "Burned Out"
+
+    def get_pos(self):
+        return self.pos
\ No newline at end of file
diff --git a/examples/ForestFire/ForestFire.py b/examples/ForestFire/forest_fire/model.py
similarity index 64%
rename from examples/ForestFire/ForestFire.py
rename to examples/ForestFire/forest_fire/model.py
index cdef0a65..10ad4abf 100644
--- a/examples/ForestFire/ForestFire.py
+++ b/examples/ForestFire/forest_fire/model.py
@@ -1,59 +1,24 @@
 import random
 
-from mesa import Model, Agent
-from mesa.time import RandomActivation
-from mesa.space import Grid
+from examples.ForestFire.forest_fire.agent import TreeCell
+from mesa import Model
 from mesa.datacollection import DataCollector
-
-
-class TreeCell(Agent):
-    '''
-    A tree cell.
-
-    Attributes:
-        x, y: Grid coordinates
-        condition: Can be "Fine", "On Fire", or "Burned Out"
-        unique_id: (x,y) tuple.
-
-    unique_id isn't strictly necessary here, but it's good
-    practice to give one to each agent anyway.
-    '''
-    def __init__(self, pos):
-        '''
-        Create a new tree.
-        Args:
-            pos: The tree's coordinates on the grid.
-        '''
-        self.pos = pos
-        self.unique_id = pos
-        self.condition = "Fine"
-
-    def step(self, model):
-        '''
-        If the tree is on fire, spread it to fine trees nearby.
-        '''
-        if self.condition == "On Fire":
-            for neighbor in model.grid.neighbor_iter(self.pos):
-                if neighbor.condition == "Fine":
-                    neighbor.condition = "On Fire"
-            self.condition = "Burned Out"
-
-    def get_pos(self):
-        return self.pos
+from mesa.space import Grid
+from mesa.time import RandomActivation
 
 
 class ForestFire(Model):
-    '''
+    """
     Simple Forest Fire model.
-    '''
+    """
     def __init__(self, height, width, density):
-        '''
+        """
         Create a new forest fire model.
 
         Args:
             height, width: The size of the grid to model
             density: What fraction of grid cells have a tree in them.
-        '''
+        """
         # Initialize model parameters
         self.height = height
         self.width = width
@@ -81,9 +46,9 @@ class ForestFire(Model):
         self.running = True
 
     def step(self):
-        '''
+        """
         Advance the model by one step.
-        '''
+        """
         self.schedule.step()
         self.datacollector.collect(self)
 
@@ -93,9 +58,9 @@ class ForestFire(Model):
 
     @staticmethod
     def count_type(model, tree_condition):
-        '''
+        """
         Helper method to count trees in a given condition in a given model.
-        '''
+        """
         count = 0
         for tree in model.schedule.agents:
             if tree.condition == tree_condition:
diff --git a/examples/ForestFire/ForestFireVisualization.py b/examples/ForestFire/forest_fire/server.py
similarity index 80%
rename from examples/ForestFire/ForestFireVisualization.py
rename to examples/ForestFire/forest_fire/server.py
index 4b4d2a43..101814fc 100644
--- a/examples/ForestFire/ForestFireVisualization.py
+++ b/examples/ForestFire/forest_fire/server.py
@@ -1,9 +1,9 @@
-from ForestFire import ForestFire
+from forest_fire.model import ForestFire
 from mesa.visualization.modules import CanvasGrid, ChartModule
 from mesa.visualization.ModularVisualization import ModularServer
 
 
-def forestfire_draw(tree):
+def forest_fire_portrayal(tree):
     if tree is None:
         return
     portrayal = {"Shape": "rect", "w": 1, "h": 1, "Filled": "true", "Layer": 0}
@@ -16,11 +16,10 @@ def forestfire_draw(tree):
     portrayal["Color"] = colors[tree.condition]
     return portrayal
 
-canvas_element = CanvasGrid(forestfire_draw, 100, 100, 500, 500)
+canvas_element = CanvasGrid(forest_fire_portrayal, 100, 100, 500, 500)
 tree_chart = ChartModule([{"Label": "Fine", "Color": "green"},
                           {"Label": "On Fire", "Color": "red"},
                           {"Label": "Burned Out", "Color": "black"}])
 
 server = ModularServer(ForestFire, [canvas_element, tree_chart], "Forest Fire",
-                       100, 100, 0.65)
-server.launch()
+                       100, 100, 0.65)
\ No newline at end of file
diff --git a/examples/ForestFire/readme.md b/examples/ForestFire/readme.md
index fae59122..6f40fa0c 100644
--- a/examples/ForestFire/readme.md
+++ b/examples/ForestFire/readme.md
@@ -6,10 +6,10 @@ The [forest fire model](http://en.wikipedia.org/wiki/Forest-fire_model) is a sim
 
 ## How to Run
 
-Launch an interactive server by Running ``ForestFireVisualization.py``:
+Launch an interactive server by Running ``run.py``:
 
 ```
-    $ python ForestFireVisualization.py
+    $ python run.py
 ```
 
 Then open your browser to [http://127.0.0.1:8888/](http://127.0.0.1:8888/) and press Reset, then Run. 
@@ -18,7 +18,7 @@ To view and run the model analyses, use the ``Forest Fire Model`` Notebook.
 
 ## Files
 
-### ``ForestFire.py``
+### ``forest_fire/model.py``
 
 This defines the model. There is one agent class, **TreeCell**. Each TreeCell object which has (x, y) coordinates on the grid, and its condition is *Fine* by default. Every step, if the tree's condition is *On Fire*, it spreads the fire to any *Fine* trees in its [Von Neumann neighborhood](http://en.wikipedia.org/wiki/Von_Neumann_neighborhood) before changing its own condition to *Burned Out*.
 
@@ -27,9 +27,9 @@ The **ForestFire** class is the model container. It is instantiated with width a
 Each step of the model, trees are activated in random order, spreading the fire and burning out. This continues until there are no more trees on fire -- the fire has completely burned out.
 
 
-### ``ForestFireVisualization.py``
+### ``forest_fire/server.py``
 
-This code defines and launches the in-browser visualization for the ForestFire model. It includes the **forestfire_draw** method, which takes a TreeCell object as an argument and turns it into a portrayal to be drawn in the browser. Each tree is drawn as a rectangle filling the entire cell, with a color based on its condition. *Fine* trees are green, *On Fire* trees red, and *Burned Out* trees are black.
+This code defines and launches the in-browser visualization for the ForestFire model. It includes the **forest_fire_draw** method, which takes a TreeCell object as an argument and turns it into a portrayal to be drawn in the browser. Each tree is drawn as a rectangle filling the entire cell, with a color based on its condition. *Fine* trees are green, *On Fire* trees red, and *Burned Out* trees are black.
 
 ## Further Reading
 
diff --git a/examples/ForestFire/run.py b/examples/ForestFire/run.py
new file mode 100644
index 00000000..0a3f2e2d
--- /dev/null
+++ b/examples/ForestFire/run.py
@@ -0,0 +1,3 @@
+from examples.ForestFire.forest_fire.server import server
+
+server.launch()
\ No newline at end of file
diff --git a/examples/Readme.md b/examples/Readme.md
index d7a8e056..91399dc1 100644
--- a/examples/Readme.md
+++ b/examples/Readme.md
@@ -2,6 +2,7 @@
 
 This directory contains example models meant to test and demonstrate Mesa's features, and provide demonstrations for how to build and analyze agent-based models. For more information on each model, see its own Readme and documentation.
 
+
 ### Color Patches
 A cellular automaton model where agents opinions are influenced by that of their neighbors. As the model evolves, color patches representing the prevailing opinion in a given area expand, contract, and sometimes disappear.
 
@@ -29,6 +30,3 @@ Completed code to go along with the [tutorial]() on making a simple model of how
 
 ### WolfSheep
 Implementation of an ecological model of predation and reproduction, based on the NetLogo [Wolf Sheep Predation model](http://ccl.northwestern.edu/netlogo/models/WolfSheepPredation).
-
-### Basic
-Example of grid display and direction showing agents in the form of arrow-head shape. 
diff --git a/mesa/__init__.py b/mesa/__init__.py
index d370701e..7d35c10d 100644
--- a/mesa/__init__.py
+++ b/mesa/__init__.py
@@ -5,14 +5,57 @@ Mesa Agent-Based Modeling Framework
 Core Objects: Model, and Agent.
 
 """
-
-from .model import Model
-from .agent import Agent
-
-
-__all__ = ["Model", "Agent"]
+import datetime as dt
+import random
 
 __title__ = 'mesa'
 __version__ = '0.7.5'
 __license__ = 'Apache 2.0'
 __copyright__ = 'Copyright 2015 Project Mesa Team'
+
+
+class Model:
+    """ Base class for models. """
+    def __init__(self, seed=None):
+        """ Create a new model. Overload this method with the actual code to
+        start the model.
+
+        Args:
+            seed: seed for the random number generator
+
+        Attributes:
+            schedule: schedule object
+            running: a bool indicating if the model should continue running
+
+        """
+        if seed is None:
+            self.seed = dt.datetime.now()
+        else:
+            self.seed = seed
+        random.seed(seed)
+        self.running = True
+        self.schedule = None
+
+    def run_model(self):
+        """ Run the model until the end condition is reached. Overload as
+        needed.
+
+        """
+        while self.running:
+            self.step()
+
+    def step(self):
+        """ A single step. Fill in here. """
+        pass
+
+
+class Agent:
+    """ Base class for a model agent. """
+    def __init__(self, unique_id, model):
+        """ Create a new agent. """
+        self.unique_id = unique_id
+        self.model = model
+
+    def step(self, model):
+        """ A single step of the agent. """
+        pass
diff --git a/mesa/agent.py b/mesa/agent.py
deleted file mode 100644
index caa872be..00000000
--- a/mesa/agent.py
+++ /dev/null
@@ -1,19 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-The agent class for Mesa framework.
-
-Core Objects: Agent
-
-"""
-
-
-class Agent:
-    """ Base class for a model agent. """
-    def __init__(self, unique_id, model):
-        """ Create a new agent. """
-        self.unique_id = unique_id
-        self.model = model
-
-    def step(self, model):
-        """ A single step of the agent. """
-        pass
diff --git a/mesa/model.py b/mesa/model.py
deleted file mode 100644
index 6e4d4241..00000000
--- a/mesa/model.py
+++ /dev/null
@@ -1,44 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-The model class for Mesa framework.
-
-Core Objects: Model
-
-"""
-import datetime as dt
-import random
-
-
-class Model:
-    """ Base class for models. """
-    def __init__(self, seed=None):
-        """ Create a new model. Overload this method with the actual code to
-        start the model.
-
-        Args:
-            seed: seed for the random number generator
-
-        Attributes:
-            schedule: schedule object
-            running: a bool indicating if the model should continue running
-
-        """
-        if seed is None:
-            self.seed = dt.datetime.now()
-        else:
-            self.seed = seed
-        random.seed(seed)
-        self.running = True
-        self.schedule = None
-
-    def run_model(self):
-        """ Run the model until the end condition is reached. Overload as
-        needed.
-
-        """
-        while self.running:
-            self.step()
-
-    def step(self):
-        """ A single step. Fill in here. """
-        pass
diff --git a/mesa/space.py b/mesa/space.py
index 4e96a21b..4695b953 100644
--- a/mesa/space.py
+++ b/mesa/space.py
@@ -41,9 +41,9 @@ def accept_tuple_argument(wrapped_function):
 class Grid:
     """ Base class for a square grid.
 
-    Grid cells are indexed by [x][y], where [0][0] is assumed to be the
-    bottom-left and [width-1][height-1] is the top-right. If a grid is
-    toroidal, the top and bottom, and left and right, edges wrap to each other
+    Grid cells are indexed by [y][x], where [0][0] is assumed to be -- top-left
+    and [height-1][width-1] is the bottom-right. If a grid is toroidal, the top
+    and bottom, and left and right, edges wrap to each other
 
     Properties:
         width, height: The grid's width and height.
@@ -59,24 +59,21 @@ class Grid:
         coord_iter: Returns coordinates as well as cell contents.
         place_agent: Positions an agent on the grid, and set its pos variable.
         move_agent: Moves an agent from its current position to a new position.
-        iter_neighborhood: Returns an iterator over cell coordinates that are
-        in the neighborhood of a certain point.
+        iter_neighborhood: Returns an iterator over cell coordinates that are in the
+        neighborhood of a certain point.
         torus_adj: Converts coordinate, handles torus looping.
-        out_of_bounds: Determines whether position is off the grid, returns
-        the out of bounds coordinate.
-        iter_cell_list_contents: Returns an iterator of the contents of the
-        cells identified in cell_list.
-        get_cell_list_contents: Returns a list of the contents of the cells
-        identified in cell_list.
+        out_of_bounds: Determines whether position is off the grid, returns the out of bounds coordinate.
+        iter_cell_list_contents: Returns an iterator of the contents of the cells identified in cell_list.
+        get_cell_list_contents: Returns a list of the contents of the cells identified in cell_list.
         remove_agent: Removes an agent from the grid.
         is_cell_empty: Returns a bool of the contents of a cell.
 
     """
-    def __init__(self, width, height, torus):
+    def __init__(self, height, width, torus):
         """ Create a new grid.
 
         Args:
-            width, height: The width and height of the grid
+            height, width: The height and width of the grid
             torus: Boolean whether the grid wraps or not.
 
         """
@@ -86,11 +83,11 @@ class Grid:
 
         self.grid = []
 
-        for x in range(self.width):
-            col = []
-            for y in range(self.height):
-                col.append(self.default_val())
-            self.grid.append(col)
+        for y in range(self.height):
+            row = []
+            for x in range(self.width):
+                row.append(self.default_val())
+            self.grid.append(row)
 
     @staticmethod
     def default_val():
@@ -107,9 +104,9 @@ class Grid:
 
     def coord_iter(self):
         """ An iterator that returns coordinates as well as cell contents. """
-        for row in range(self.width):
-            for col in range(self.height):
-                yield self.grid[row][col], row, col    # agent, x, y
+        for row in range(self.height):
+            for col in range(self.width):
+                yield self.grid[row][col], col, row  # agent, x, y
 
     def neighbor_iter(self, pos, moore=True):
         """ Iterate over position neighbors.
@@ -139,10 +136,10 @@ class Grid:
             radius: radius, in cells, of neighborhood to get.
 
         Returns:
-            A list of coordinate tuples representing the neighborhood. For
-            example with radius 1, it will return list with number of elements
-            equals at most 9 (8) if Moore, 5 (4) if Von Neumann (if not
-            including the center).
+            A list of coordinate tuples representing the neighborhood;
+                With radius 1, at most 9 if
+                Moore, 5 if Von Neumann
+                (8 and 4 if not including the center).
 
         """
         x, y = pos
@@ -159,7 +156,7 @@ class Grid:
                     continue
                 # Skip if not a torus and new coords out of bounds.
                 if not self.torus and (not (0 <= dx + x < self.width) or
-                                       not (0 <= dy + y < self.height)):
+                        not (0 <= dy + y < self.height)):
                     continue
 
                 px = self.torus_adj(x + dx, self.width)
@@ -253,10 +250,7 @@ class Grid:
         return coord
 
     def out_of_bounds(self, pos):
-        """
-        Determines whether position is off the grid, returns the out of
-        bounds coordinate.
-        """
+        """ Determines whether position is off the grid, returns the out of bounds coordinate."""
         x, y = pos
         return x < 0 or x >= self.width or y < 0 or y >= self.height
 
@@ -271,7 +265,7 @@ class Grid:
 
         """
         return (
-            self[x][y] for x, y in cell_list if not self.is_cell_empty((x, y)))
+            self[y][x] for x, y in cell_list if not self.is_cell_empty((x, y)))
 
     @accept_tuple_argument
     def get_cell_list_contents(self, cell_list):
@@ -307,32 +301,32 @@ class Grid:
     def _place_agent(self, pos, agent):
         """ Place the agent at the correct location. """
         x, y = pos
-        self.grid[x][y] = agent
+        self.grid[y][x] = agent
 
     def _remove_agent(self, pos, agent):
         """ Remove the agent from the given location. """
         x, y = pos
-        self.grid[x][y] = None
+        self.grid[y][x] = None
 
     def is_cell_empty(self, pos):
         """ Returns a bool of the contents of a cell. """
         x, y = pos
-        return True if self.grid[x][y] == self.default_val() else False
+        return True if self.grid[y][x] == self.default_val() else False
 
 
 class SingleGrid(Grid):
     """ Grid where each cell contains exactly at most one object. """
     empties = []
 
-    def __init__(self, width, height, torus):
+    def __init__(self, height, width, torus):
         """ Create a new single-item grid.
 
         Args:
-            width, height: The width and width of the grid
+            height, width: The height and width of the grid
             torus: Boolean whether the grid wraps or not.
 
         """
-        super().__init__(width, height, torus)
+        super().__init__(height, width, torus)
         # Add all cells to the empties list.
         self.empties = list(itertools.product(
                             *(range(self.width), range(self.height))))
@@ -394,9 +388,9 @@ class SingleGrid(Grid):
 class MultiGrid(Grid):
     """ Grid where each cell can contain more than one object.
 
-    Grid cells are indexed by [x][y], where [0][0] is assumed to be at
-    bottom-left and [width-1][height-1] is the top-right. If a grid is
-    toroidal, the top and bottom, and left and right, edges wrap to each other.
+    Grid cells are indexed by [y][x], where [0][0] is assumed to be -- top-left
+    and [height-1][width-1] is the bottom-right. If a grid is toroidal, the top
+    and bottom, and left and right, edges wrap to each other.
 
     Each grid cell holds a set object.
 
@@ -418,12 +412,12 @@ class MultiGrid(Grid):
     def _place_agent(self, pos, agent):
         """ Place the agent at the correct location. """
         x, y = pos
-        self.grid[x][y].add(agent)
+        self.grid[y][x].add(agent)
 
     def _remove_agent(self, pos, agent):
         """ Remove the agent from the given location. """
         x, y = pos
-        self.grid[x][y].remove(agent)
+        self.grid[y][x].remove(agent)
 
     @accept_tuple_argument
     def iter_cell_list_contents(self, cell_list):
@@ -436,7 +430,7 @@ class MultiGrid(Grid):
 
         """
         return itertools.chain.from_iterable(
-            self[x][y] for x, y in cell_list if not self.is_cell_empty((x, y)))
+            self[y][x] for x, y in cell_list if not self.is_cell_empty((x, y)))
 
 
 class ContinuousSpace:
@@ -477,7 +471,7 @@ class ContinuousSpace:
         self.cell_width = (self.x_max - self.x_min) / grid_width
         self.cell_height = (self.y_max - self.y_min) / grid_height
 
-        self._grid = MultiGrid(grid_width, grid_height, torus)
+        self._grid = MultiGrid(grid_height, grid_width, torus)
 
     def place_agent(self, agent, pos):
         """ Place a new agent in the space.
@@ -531,7 +525,7 @@ class ContinuousSpace:
         cell_radius = math.ceil(radius / scale)
         cell_pos = self._point_to_cell(pos)
         possible_objs = self._grid.get_neighbors(cell_pos,
-                                                 True, True, cell_radius)
+                                              True, True, cell_radius)
         neighbors = []
         # Iterate over candidates and check actual distance.
         for obj in possible_objs:
diff --git a/mesa/time.py b/mesa/time.py
index 23c354b8..ab510fae 100644
--- a/mesa/time.py
+++ b/mesa/time.py
@@ -142,7 +142,7 @@ class StagedActivation(BaseScheduler):
     stage_time = 1
 
     def __init__(self, model, stage_list=["step"], shuffle=False,
-                 shuffle_between_stages=False):
+            shuffle_between_stages=False):
         """ Create an empty Staged Activation schedule.
 
         Args:
diff --git a/mesa/visualization/ModularVisualization.md b/mesa/visualization/ModularVisualization.md
index f0623ba6..23f1ebe9 100644
--- a/mesa/visualization/ModularVisualization.md
+++ b/mesa/visualization/ModularVisualization.md
@@ -28,21 +28,15 @@ Mesa already comes with some pre-built modules. Using the built-ins allow you to
 One built-in module is **CanvasGrid**, which you can use to visualize objects located on grid cells. The CanvasGrid will cover a majority of agent-based models, particularly the simpler ones.
 
 CanvasGrid iterates over every object in every cell of your model's grid (it assumes that your model has a grid named **grid**) and converts it into a dictionary which defines how it will be drawn. It does this via a **portrayal_method**: a function which the user defines, which takes an object as an input and outputs a dictionary with the following keys:
-    "Shape": Can be "circle", "rect" or "arrowHead"
+    "Shape": Can be either "circle" or "rect"
         For Circles:
             "r": The radius, defined as a fraction of cell size. r=1 will fill the entire cell.
         For rectangles:
             "w", "h": The width and height of the rectangle, which are in fractions of cell width and height.
-        For arrowHead:
-            "scale": Proportion scaling as a fraction of cell size. shape=0.5 will fit the arrowHead into half of the cell.
-            "heading0": represents x direction unit vector.
-            "heading1": represents y direction unit vector.
     "Color": The color to draw the shape in; needs to be a valid HTML color, e.g."Red" or "#AA08F8"
     "Filled": either "true" or "false", and determines whether the shape is filled or not.
     "Layer": Layer number of 0 or above; higher-numbered layers are drawn above lower-numbered layers.
-    "text": Text to overlay on top of the shape. Normally, agent's unique_id is used .
-    "text_color": Color of the text overlay.
-    (Shapes also have "x" and "y" coordinates, for the x and y of the grid cell in which it is, but CanvasGrid adds those automatically).
+    (Shapes also have "x" and "x" coordinates, for the x and y of the grid cell in which it is, but CanvasGrid adds those automatically).
 
 For example, suppose for a Schelling model, we want to draw all agents as circles; red ones for the majority (agent type=0), and blue ones for the minority (agent type=1). The function to do this might look like this:
 
@@ -156,3 +150,6 @@ Data to visualize arrive over the websocket as a list. For each index of the lis
 Currently, module JavaScript files live in the *mesa/visualization/templates* directory, and the Python files live in *mesa/visualization/modules*.
 
 When creating a new module, the Python and JavaScript code need to be written in synch: the module Python-side **render** method needs to output data in the exact same format that the JavaScript **render** function receives as an input.
+
+
+
diff --git a/mesa/visualization/modules/CanvasGridVisualization.py b/mesa/visualization/modules/CanvasGridVisualization.py
index 5f0a70fc..450c3b39 100644
--- a/mesa/visualization/modules/CanvasGridVisualization.py
+++ b/mesa/visualization/modules/CanvasGridVisualization.py
@@ -52,26 +52,26 @@ class CanvasGrid(VisualizationElement):
     """
     package_includes = ["GridDraw.js", "CanvasModule.js"]
     portrayal_method = None  # Portrayal function
-    canvas_width = 500
     canvas_height = 500
+    canvas_width = 500
 
-    def __init__(self, portrayal_method, grid_width, grid_height,
-                 canvas_width=500, canvas_height=500):
+    def __init__(self, portrayal_method, grid_height, grid_width,
+                 canvas_height=500, canvas_width=500):
         """ Instantiate a new CanvasGrid.
 
         Args:
             portrayal_method: function to convert each object on the grid to
                               a portrayal, as described above.
-            grid_width, grid_height: Size of the grid, in cells.
+            grid_height, grid_width: Size of the grid, in cells.
             canvas_height, canvas_width: Size of the canvas to draw in the
                                          client, in pixels. (default: 500x500)
 
         """
         self.portrayal_method = portrayal_method
-        self.grid_width = grid_width
         self.grid_height = grid_height
-        self.canvas_width = canvas_width
+        self.grid_width = grid_width
         self.canvas_height = canvas_height
+        self.canvas_width = canvas_width
 
         new_element = ("new CanvasModule({}, {}, {}, {})"
             .format(self.canvas_width, self.canvas_height,
@@ -81,8 +81,8 @@ class CanvasGrid(VisualizationElement):
 
     def render(self, model):
         grid_state = defaultdict(list)
-        for x in range(model.grid.width):
-            for y in range(model.grid.height):
+        for y in range(model.grid.height):
+            for x in range(model.grid.width):
                 cell_objects = model.grid.get_cell_list_contents([(x, y)])
                 for obj in cell_objects:
                     portrayal = self.portrayal_method(obj)
diff --git a/mesa/visualization/templates/CanvasModule.js b/mesa/visualization/templates/CanvasModule.js
index 4861c3e3..43e537b8 100644
--- a/mesa/visualization/templates/CanvasModule.js
+++ b/mesa/visualization/templates/CanvasModule.js
@@ -11,7 +11,7 @@ var CanvasModule = function(canvas_width, canvas_height, grid_width, grid_height
 
 	// Create the context and the drawing controller:
 	var context = canvas.getContext("2d");
-	var canvasDraw = new GridVisualization(canvas_width, canvas_height, grid_width, grid_height, context);
+	var canvasDraw = new GridVisualization(canvas_width, canvas_height, grid_height, grid_width, context);
 
 	this.render = function(data) {
 		canvasDraw.resetCanvas();
@@ -24,4 +24,4 @@ var CanvasModule = function(canvas_width, canvas_height, grid_width, grid_height
 		canvasDraw.resetCanvas();
 	};
 
-};
+};
\ No newline at end of file
diff --git a/mesa/visualization/templates/GridDraw.js b/mesa/visualization/templates/GridDraw.js
index 3ffd5cd3..a195c6b5 100644
--- a/mesa/visualization/templates/GridDraw.js
+++ b/mesa/visualization/templates/GridDraw.js
@@ -1,82 +1,77 @@
-/**
+/*
 Mesa Canvas Grid Visualization
 ====================================================================
 
-This is JavaScript code to visualize a Mesa Grid or MultiGrid state using the
+This is JavaScript code to visualize a Mesa Grid or MultiGrid state using the 
 HTML5 Canvas. Here's how it works:
 
 On the server side, the model developer will have assigned a portrayal to each
 agent type. The visualization then loops through the grid, for each object adds
-a JSON object to an inner list (keyed on layer) of lists to be sent to the
-browser.
+a JSON object to an inner list (keyed on layer) of lists to be sent to the browser. 
 
-Each JSON object to be drawn contains the following fields: Shape (currently
-only rectanges and circles are supported), x, y, Color, Filled (boolean),
-Layer; circles also get a Radius, while rectangles get x and y sizes. The
-latter values are all between [0, 1] and get scaled to the grid cell.
+Each JSON object to be drawn contains the following fields: Shape (currently 
+only rectanges and circles are supported), x, y, Color, Filled (boolean), Layer; 
+circles also get a Radius, while rectangles get x and y sizes. The latter values
+are all between [0, 1] and get scaled to the grid cell.
 
-The browser (this code, in fact) then iteratively draws them in, one layer at a
-time. Thus, it should be possible to turn different layers on and off.
+The browser (this code, in fact) then iteratively draws them in, one layer at a time. Thus, it 
+should be possible to turn different layers on and off. 
 
-Here's a sample input, for a 2x2 grid with one layer being cell colors and the
-other agent locations, represented by circles:
+Here's a sample input, for a 2x2 grid with one layer being cell colors and the other agent 
+locations, represented by circles:
 
 {"Shape": "rect", "x": 0, "y": 0, "Color": "#00aa00", "Filled": "true", "Layer": 0}
 
-{0:[
-        {"Shape": "rect", "x": 0, "y": 0, "w": 1, "h": 1, "Color": "#00aa00", "Filled": "true", "Layer": 0},
-	{"Shape": "rect", "x": 0, "y": 1, "w": 1, "h": 1, "Color": "#00aa00", "Filled": "true", "Layer": 0},
-	{"Shape": "rect", "x": 1, "y": 0, "w": 1, "h": 1, "Color": "#00aa00", "Filled": "true", "Layer": 0},
-	{"Shape": "rect", "x": 1, "y": 1, "w": 1, "h": 1, "Color": "#00aa00", "Filled": "true", "Layer": 0}
+{0:[{"Shape": "rect", "x": 0, "y": 0, "w": 1, "h": 1, "Color": "#00aa00", "Filled": "true", "Layer": 0},
+	{"Shape": "rect", "x": 0, "y": 0, "w": 1, "h": 1, "Color": "#00aa00", "Filled": "true", "Layer": 0},
+	{"Shape": "rect", "x": 0, "y": 0, "w": 1, "h": 1, "Color": "#00aa00", "Filled": "true", "Layer": 0},
+	{"Shape": "rect", "x": 0, "y": 0, "w": 1, "h": 1, "Color": "#00aa00", "Filled": "true", "Layer": 0} 
    ],
- 1:[
+ 1: [
 	{"Shape": "circle", "x": 0, "y": 0, "r": 0.5, "Color": "#AAAAAA", "Filled": "true", "Layer": 1, "text": 'A', "text_color": "white"},
 	{"Shape": "circle", "x": 1, "y": 1, "r": 0.5, "Color": "#AAAAAA", "Filled": "true", "Layer": 1, "text": 'B', "text_color": "white"}
-	{"Shape": "arrowHead", "x": 1, "y": 0, "heading0": -1, heading1: 0, "scale": 0.5, "Color": "green", "Filled": "true", "Layer": 1, "text": 'C', "text_color": "white"}
    ]
 }
 
 */
 
-var GridVisualization = function(width, height, gridWidth, gridHeight, context) {
-	var width = width;
+var GridVisualization = function(height, width, gridHeight, gridWidth, context) {
 	var height = height;
-	var gridWidth = gridWidth;
+	var width = width;
 	var gridHeight = gridHeight;
+	var gridWidth = gridWidth;
 	var context = context;
 
 	// Find cell size:
-	var cellWidth = Math.floor(width / gridWidth);
 	var cellHeight = Math.floor(height / gridHeight);
+	var cellWidth = Math.floor(width / gridWidth);
 
-        // Find max radius of the circle that can be inscribed (fit) into the
-        // cell of the grid.
 	var maxR = Math.min(cellHeight, cellWidth)/2 - 1;
 
-	// Calls the appropriate shape(agent)
-        this.drawLayer = function(portrayalLayer) {
+
+	this.drawLayer = function(portrayalLayer) {
 		for (var i in portrayalLayer) {
 			var p = portrayalLayer[i];
 			if (p.Shape == "rect")
-				this.drawRectangle(p.x, p.y, p.w, p.h, p.Color, p.Filled, p.text, p.text_color);
-			else if (p.Shape == "circle")
+				this.drawRectange(p.x, p.y, p.w, p.h, p.Color, p.Filled, p.text, p.text_color);
+			if (p.Shape == "circle")
 				this.drawCircle(p.x, p.y, p.r, p.Color, p.Filled, p.text, p.text_color);
-                        else if (p.Shape == "arrowHead")
-				this.drawArrowHead(p.x, p.y, p.heading0, p.heading1, p.scale, p.Color, p.Filled, p.text, p.text_color);
 		}
 	};
 
-	// DRAWING METHODS
-	// =====================================================================
 
+
+	//   DRAWING METHODS
+	//  =====================================================================
+	
 	/**
-	Draw a circle in the specified grid cell.
-	x, y: Grid coords
-	r: Radius, as a multiple of cell size
-	color: Code for the fill color
-	fill: Boolean for whether or not to fill the circle.
-        text: Inscribed text in rectangle.
-        text_color: Color of the inscribed text.
+		Draw a circle in the specified grid cell.
+		x, y: Grid coords
+		r: Radius, as a multiple of cell size
+		color: Code for the fill color
+		fill: Boolean for whether or not to fill the circle.
+                text: Inscribed text in rectangle.
+                text_color: Color of the inscribed text.
         */
 	this.drawCircle = function(x, y, radius, color, fill, text, text_color) {
 		var cx = (x + 0.5) * cellWidth;
@@ -94,7 +89,7 @@ var GridVisualization = function(width, height, gridWidth, gridHeight, context)
 			context.fillStyle = color;
 			context.fill();
 		}
-
+                
                 // This part draws the text inside the Circle
                 if (text !== undefined) {
                         context.fillStyle = text_color;
@@ -106,16 +101,15 @@ var GridVisualization = function(width, height, gridWidth, gridHeight, context)
 	};
 
 	/**
-	Draw a rectangle in the specified grid cell.
-	x, y: Grid coords
-	w, h: Width and height, [0, 1]
-	color: Color for the rectangle
-	fill: Boolean, whether to fill or not.
-        text: Inscribed text in rectangle.
-        text_color: Color of the inscribed text.
+		Draw a rectangle in the specified grid cell.
+		x, y: Grid coords
+		w, h: Width and height, [0, 1]
+		color: Color for the rectangle
+		fill: Boolean, whether to fill or not.
+                text: Inscribed text in rectangle.
+                text_color: Color of the inscribed text.
 	*/
-	this.drawRectangle = function(x, y, w, h, color, fill, text, text_color) {
-		y = gridHeight - y - 1;
+	this.drawRectange = function(x, y, w, h, color, fill, text, text_color) {
 		context.beginPath();
 		var dx = w * cellWidth;
 		var dy = h * cellHeight;
@@ -142,89 +136,8 @@ var GridVisualization = function(width, height, gridWidth, gridHeight, context)
                 }
 	};
 
-        /**
-	Draw an arrow head in the specified grid cell.
-	x, y: Grid coords
-	s: Scaling of the arrowHead with respect to cell size[0, 1]
-	color: Color for the shape
-	fill: Boolean, whether to fill or not.
-        text: Inscribed text in shape.
-        text_color: Color of the inscribed text.
-	*/
-        this.drawArrowHead = function(x, y, heading0, heading1, scale, color, fill, text, text_color) {
-                y = gridHeight - y -1;
-                arrowR = maxR * scale;
-		var cx = (x + 0.5) * cellWidth;
-		var cy = (y + 0.5) * cellHeight;
-                if (heading0 === 0 && heading1 === 1) {
-                        p1_x = cx;
-                        p1_y = cy - arrowR;
-                        p2_x = cx - arrowR;
-                        p2_y = cy + arrowR;
-                        p3_x = cx;
-                        p3_y = cy + 0.8*arrowR;
-                        p4_x = cx + arrowR;
-                        p4_y = cy + arrowR;
-                }
-                else if (heading0 === 1 && heading1 === 0) {
-                        p1_x = cx + arrowR;
-                        p1_y = cy;
-                        p2_x = cx - arrowR;
-                        p2_y = cy - arrowR;
-                        p3_x = cx - 0.8*arrowR;
-                        p3_y = cy;
-                        p4_x = cx - arrowR;
-                        p4_y = cy + arrowR;
-                }
-                else if (heading0 === 0 && heading1 === (-1)) {
-                        p1_x = cx;
-                        p1_y = cy + arrowR;
-                        p2_x = cx - arrowR;
-                        p2_y = cy - arrowR;
-                        p3_x = cx;
-                        p3_y = cy - 0.8*arrowR;
-                        p4_x = cx + arrowR;
-                        p4_y = cy - arrowR;
-                }
-                else if (heading0 === (-1) && heading1 === 0) {
-                        p1_x = cx - arrowR;
-                        p1_y = cy;
-                        p2_x = cx + arrowR;
-                        p2_y = cy - arrowR;
-                        p3_x = cx + 0.8*arrowR;
-                        p3_y = cy;
-                        p4_x = cx + arrowR;
-                        p4_y = cy + arrowR;
-                }
-
-		context.beginPath();
-                context.moveTo(p1_x, p1_y);
-                context.lineTo(p2_x, p2_y);
-                context.lineTo(p3_x, p3_y);
-                context.lineTo(p4_x, p4_y);
-		context.closePath();
-
-		context.strokeStyle = color;
-		context.stroke();
-
-		if (fill) {
-			context.fillStyle = color;
-			context.fill();
-		}
-
-                // This part draws the text inside the ArrowHead
-                if (text !== undefined) {
-        		var cx = (x + 0.5) * cellWidth;
-                  	var cy = (y + 0.5) * cellHeight;
-                        context.fillStyle = text_color
-                        context.textAlign = 'center';
-                        context.textBaseline= 'middle';
-                        context.fillText(text, cx, cy);
-                }
-        };
-
 	/**
-        Draw Grid lines in the full gird
+		Draw Grid lines in the full gird
         */
 
 	this.drawGridLines = function() {
@@ -248,7 +161,7 @@ var GridVisualization = function(width, height, gridWidth, gridHeight, context)
 	};
 
 	this.resetCanvas = function() {
-		context.clearRect(0, 0, width, height);
+		context.clearRect(0, 0, height, width);
 		context.beginPath();
 	};
 


Test patch:
diff --git a/tests/test_batchrunner.py b/tests/test_batchrunner.py
index a900f6f4..fb917fea 100644
--- a/tests/test_batchrunner.py
+++ b/tests/test_batchrunner.py
@@ -61,7 +61,7 @@ class TestBatchRunner(unittest.TestCase):
         self.params = {
             'model_param': range(3),
             'agent_param': [1, 8],
-        }
+            }
         self.iterations = 17
         self.batch = BatchRunner(
             MockModel,
diff --git a/tests/test_grid.py b/tests/test_grid.py
index e2fd7f27..20f9806f 100644
--- a/tests/test_grid.py
+++ b/tests/test_grid.py
@@ -6,19 +6,10 @@ import unittest
 from mesa.space import Grid, SingleGrid, MultiGrid
 
 # Initial agent positions for testing
-#
-# --- visual aid ----
-#   0 0 0
-#   1 1 0
-#   0 1 0
-#   1 0 1
-#   0 0 1
-# -------------------
-TEST_GRID = [
-    [0, 1, 0, 1, 0],
-    [0, 0, 1, 1, 0],
-    [1, 1, 0, 0, 0]
-]
+#              X ---- >
+TEST_GRID = [[0, 1, 0, 1, 0],   # Y
+             [0, 1, 1, 0, 0],   # |
+             [0, 0, 0, 1, 0]]   # V
 
 
 class MockAgent:
@@ -41,14 +32,12 @@ class TestBaseGrid(unittest.TestCase):
         '''
         Create a test non-toroidal grid and populate it with Mock Agents
         '''
-        width = 3    # width of grid
-        height = 5    # height of grid
-        self.grid = Grid(width, height, self.torus)
+        self.grid = Grid(3, 5, self.torus)
         self.agents = []
         counter = 0
-        for x in range(width):
-            for y in range(height):
-                if TEST_GRID[x][y] == 0:
+        for y in range(3):
+            for x in range(5):
+                if TEST_GRID[y][x] == 0:
                     continue
                 counter += 1
                 # Create and place the mock agent
@@ -62,7 +51,7 @@ class TestBaseGrid(unittest.TestCase):
         '''
         for agent in self.agents:
             x, y = agent.pos
-            assert self.grid[x][y] == agent
+            assert self.grid[y][x] == agent
 
     def test_cell_agent_reporting(self):
         '''
@@ -109,10 +98,7 @@ class TestBaseGrid(unittest.TestCase):
         neighborhood = self.grid.get_neighborhood((1, 1), moore=True)
         assert len(neighborhood) == 8
 
-        neighborhood = self.grid.get_neighborhood((1, 4), moore=False)
-        assert len(neighborhood) == 3
-
-        neighborhood = self.grid.get_neighborhood((1, 4), moore=True)
+        neighborhood = self.grid.get_neighborhood((4, 1), moore=True)
         assert len(neighborhood) == 5
 
         neighborhood = self.grid.get_neighborhood((0, 0), moore=False)
@@ -122,14 +108,14 @@ class TestBaseGrid(unittest.TestCase):
         assert len(neighbors) == 0
 
         neighbors = self.grid.get_neighbors((4, 1), moore=True)
-        assert len(neighbors) == 0
+        assert len(neighbors) == 2
 
         neighbors = self.grid.get_neighbors((1, 1), moore=False,
                                             include_center=True)
         assert len(neighbors) == 3
 
-        neighbors = self.grid.get_neighbors((1, 3), moore=False, radius=2)
-        assert len(neighbors) == 2
+        neighbors = self.grid.get_neighbors((3, 1), moore=False, radius=2)
+        assert len(neighbors) == 4
 
     def test_coord_iter(self):
         ci = self.grid.coord_iter()
@@ -143,9 +129,9 @@ class TestBaseGrid(unittest.TestCase):
         # first agent in the second space
         second = next(ci)
         assert second[0].unique_id == 1
-        assert second[0].pos == (0, 1)
-        assert second[1] == 0
-        assert second[2] == 1
+        assert second[0].pos == (1, 0)
+        assert second[1] == 1
+        assert second[2] == 0
 
 
 class TestBaseGridTorus(TestBaseGrid):
@@ -163,24 +149,24 @@ class TestBaseGridTorus(TestBaseGrid):
         neighborhood = self.grid.get_neighborhood((1, 1), moore=True)
         assert len(neighborhood) == 8
 
-        neighborhood = self.grid.get_neighborhood((1, 4), moore=True)
+        neighborhood = self.grid.get_neighborhood((4, 1), moore=True)
         assert len(neighborhood) == 8
 
         neighborhood = self.grid.get_neighborhood((0, 0), moore=False)
         assert len(neighborhood) == 4
 
-        neighbors = self.grid.get_neighbors((1, 4), moore=False)
-        assert len(neighbors) == 1
+        neighbors = self.grid.get_neighbors((4, 1), moore=False)
+        assert len(neighbors) == 0
 
-        neighbors = self.grid.get_neighbors((1, 4), moore=True)
-        assert len(neighbors) == 3
+        neighbors = self.grid.get_neighbors((4, 1), moore=True)
+        assert len(neighbors) == 2
 
         neighbors = self.grid.get_neighbors((1, 1), moore=False,
                                             include_center=True)
         assert len(neighbors) == 3
 
-        neighbors = self.grid.get_neighbors((1, 3), moore=False, radius=2)
-        assert len(neighbors) == 2
+        neighbors = self.grid.get_neighbors((3, 1), moore=False, radius=2)
+        assert len(neighbors) == 4
 
 
 class TestSingleGrid(unittest.TestCase):
@@ -195,14 +181,12 @@ class TestSingleGrid(unittest.TestCase):
         '''
         Create a test non-toroidal grid and populate it with Mock Agents
         '''
-        width = 3
-        height = 5
-        self.grid = SingleGrid(width, height, True)
+        self.grid = SingleGrid(3, 5, True)
         self.agents = []
         counter = 0
-        for x in range(width):
-            for y in range(height):
-                if TEST_GRID[x][y] == 0:
+        for y in range(3):
+            for x in range(5):
+                if TEST_GRID[y][x] == 0:
                     continue
                 counter += 1
                 # Create and place the mock agent
@@ -215,23 +199,21 @@ class TestSingleGrid(unittest.TestCase):
         Test the SingleGrid empty count and enforcement.
         '''
 
-        assert len(self.grid.empties) == 9
+        assert len(self.grid.empties) == 10
         a = MockAgent(100, None)
         with self.assertRaises(Exception):
-            self.grid._place_agent((0, 1), a)
+            self.grid._place_agent((1, 0), a)
 
         # Place the agent in an empty cell
         self.grid.position_agent(a)
-        # Test whether after placing, the empty cells are reduced by 1
         assert a.pos not in self.grid.empties
-        assert len(self.grid.empties) == 8
+        assert len(self.grid.empties) == 9
         for i in range(10):
             self.grid.move_to_empty(a)
-        assert len(self.grid.empties) == 8
+        assert len(self.grid.empties) == 9
 
         # Place agents until the grid is full
-        empty_cells = len(self.grid.empties)
-        for i in range(empty_cells):
+        for i in range(9):
             a = MockAgent(101 + i, None)
             self.grid.position_agent(a)
         assert len(self.grid.empties) == 0
@@ -242,17 +224,7 @@ class TestSingleGrid(unittest.TestCase):
         with self.assertRaises(Exception):
             self.move_to_empty(self.agents[0])
 
-
 # Number of agents at each position for testing
-# Initial agent positions for testing
-#
-# --- visual aid ----
-#   0 0 0
-#   2 0 3
-#   0 5 0
-#   1 1 0
-#   0 0 0
-# -------------------
 TEST_MULTIGRID = [[0, 1, 0, 2, 0],
                   [0, 1, 5, 0, 0],
                   [0, 0, 0, 3, 0]]
@@ -269,14 +241,12 @@ class TestMultiGrid(unittest.TestCase):
         '''
         Create a test non-toroidal grid and populate it with Mock Agents
         '''
-        width = 3
-        height = 5
-        self.grid = MultiGrid(width, height, self.torus)
+        self.grid = MultiGrid(3, 5, self.torus)
         self.agents = []
         counter = 0
-        for x in range(width):
-            for y in range(height):
-                for i in range(TEST_MULTIGRID[x][y]):
+        for y in range(3):
+            for x in range(5):
+                for i in range(TEST_MULTIGRID[y][x]):
                     counter += 1
                     # Create and place the mock agent
                     a = MockAgent(counter, None)
@@ -289,7 +259,7 @@ class TestMultiGrid(unittest.TestCase):
         '''
         for agent in self.agents:
             x, y = agent.pos
-            assert agent in self.grid[x][y]
+            assert agent in self.grid[y][x]
 
     def test_neighbors(self):
         '''
@@ -299,21 +269,21 @@ class TestMultiGrid(unittest.TestCase):
         neighborhood = self.grid.get_neighborhood((1, 1), moore=True)
         assert len(neighborhood) == 8
 
-        neighborhood = self.grid.get_neighborhood((1, 4), moore=True)
+        neighborhood = self.grid.get_neighborhood((4, 1), moore=True)
         assert len(neighborhood) == 8
 
         neighborhood = self.grid.get_neighborhood((0, 0), moore=False)
         assert len(neighborhood) == 4
 
-        neighbors = self.grid.get_neighbors((1, 4), moore=False)
+        neighbors = self.grid.get_neighbors((4, 1), moore=False)
         assert len(neighbors) == 0
 
-        neighbors = self.grid.get_neighbors((1, 4), moore=True)
+        neighbors = self.grid.get_neighbors((4, 1), moore=True)
         assert len(neighbors) == 5
 
         neighbors = self.grid.get_neighbors((1, 1), moore=False,
                                             include_center=True)
         assert len(neighbors) == 7
 
-        neighbors = self.grid.get_neighbors((1, 3), moore=False, radius=2)
+        neighbors = self.grid.get_neighbors((3, 1), moore=False, radius=2)
         assert len(neighbors) == 11
diff --git a/tests/test_time.py b/tests/test_time.py
index 81fae185..7c3164f9 100644
--- a/tests/test_time.py
+++ b/tests/test_time.py
@@ -5,8 +5,7 @@ Test the advanced schedulers.
 from unittest import TestCase
 from unittest.mock import patch
 from mesa import Model, Agent
-from mesa.time import (BaseScheduler, StagedActivation, RandomActivation,
-                       SimultaneousActivation)
+from mesa.time import BaseScheduler, StagedActivation, RandomActivation, SimultaneousActivation
 
 RANDOM = 'random'
 STAGED = 'staged'
@@ -38,14 +37,13 @@ class MockModel(Model):
         Creates a Model instance with a schedule
 
         Args:
-            shuffle (Bool): whether or not to instantiate a scheduler
-                            with shuffling.
-                            This option is only used for
-                            StagedActivation schedulers.
+            shuffle (Bool): whether or not to instantiate a scheduler with
+                            shuffling.
+                            This option is only used for StagedActivation schedulers.
 
             activation (str): which kind of scheduler to use.
-                              'random' creates a RandomActivation scheduler.
-                              'staged' creates a StagedActivation scheduler.
+                              'random' will create a RandomActivation scheduler.
+                              'staged' will create a StagedActivation scheduler.
                               The default scheduler is a BaseScheduler.
         '''
         self.log = []
@@ -53,8 +51,7 @@ class MockModel(Model):
         # Make scheduler
         if activation == STAGED:
             model_stages = ["stage_one", "stage_two"]
-            self.schedule = StagedActivation(self, model_stages,
-                                             shuffle=shuffle)
+            self.schedule = StagedActivation(self, model_stages, shuffle=shuffle)
         elif activation == RANDOM:
             self.schedule = RandomActivation(self)
         elif activation == SIMULTANEOUS: