diff --git a/analytics.py b/analytics.py
new file mode 100644
index 0000000..270361a
--- /dev/null
+++ b/analytics.py
@@ -0,0 +1,156 @@
+import math
+
+from point import Point
+
+def mean_center(points):
+    """
+    Given a set of points, compute the mean center
+
+    Parameters
+    ----------
+    points : list
+         A list of points in the form (x,y)
+
+    Returns
+    -------
+    x : float
+        Mean x coordinate
+
+    y : float
+        Mean y coordinate
+    """
+    x = None
+    y = None
+    sum_x=[]
+    sum_y=[]
+    for x_tmp,y_tmp in points:
+        sum_x.append(x_tmp)
+        sum_y.append(y_tmp)
+
+    x=float(sum(sum_x)/len(sum_x))
+    y=float(sum(sum_y)/len(sum_y))
+
+    return x, y
+
+def euclidean_distance(a, b):
+    """
+    Compute the Euclidean distance between two points
+
+    Parameters
+    ----------
+    a : tuple
+        A point in the form (x,y)
+
+    b : tuple
+        A point in the form (x,y)
+
+    Returns
+    -------
+
+    distance : float
+               The Euclidean distance between the two points
+    """
+    distance = math.sqrt((a[0] - b[0])**2 + (a[1] - b[1])**2)
+    return distance
+
+def average_nearest_neighbor_distance(points,mark=None):
+    """
+    Given a set of points, compute the average nearest neighbor.
+
+    Parameters
+    ----------
+    points : list
+             A list of points
+    mark   : str
+
+    Returns
+    -------
+    mean_d : float
+             Average nearest neighbor distance
+
+    References
+    ----------
+    Clark and Evan (1954 Distance to Nearest Neighbor as a
+     Measure of Spatial Relationships in Populations. Ecology. 35(4)
+     p. 445-453.
+    """
+    mean_d = 0
+    if mark==None:
+        points_tmp=points
+    else:
+        points_tmp=[ point for point in points if point.mark==mark]
+
+    length=len(points_tmp)
+    nearest_distances=[]
+    for i in range(length):
+        distance=[]
+        for j in range(length):
+            if i==j:
+                continue
+            else:
+                distance.append(euclidean_distance((points_tmp[i].x,points_tmp[i].y),(points_tmp[j].x,points_tmp[j].y)))
+        nearest_distances.append(min(distance))
+
+    mean_d=float(sum(nearest_distances)/len(nearest_distances))
+    return mean_d
+
+
+def minimum_bounding_rectangle(points):
+    """
+    Given a set of points, compute the minimum bounding rectangle.
+
+    Parameters
+    ----------
+    points : list
+             A list of points in the form (x,y)
+
+    Returns
+    -------
+     : list
+       Corners of the MBR in the form [xmin, ymin, xmax, ymax]
+    """
+
+    mbr = [0,0,0,0]
+
+    x_list=[]
+    y_list=[]
+    for x,y in points:
+        x_list.append(x)
+        y_list.append(y)
+    mbr=[min(x_list),min(y_list),max(x_list),max(y_list)]
+
+    return mbr
+
+
+def mbr_area(mbr):
+    """
+    Compute the area of a minimum bounding rectangle
+    """
+    area = 0
+    area=(mbr[2]-mbr[0])*(mbr[3]-mbr[1])
+    return area
+
+
+def expected_distance(area, n):
+    """
+    Compute the expected mean distance given
+    some study area.
+
+    This makes lots of assumptions and is not
+    necessarily how you would want to compute
+    this.  This is just an example of the full
+    analysis pipe, e.g. compute the mean distance
+    and the expected mean distance.
+
+    Parameters
+    ----------
+    area : float
+           The area of the study area
+
+    n : int
+        The number of points
+    """
+
+    expected = 0
+    expected =float((math.sqrt(area/n))/2)
+    return expected
diff --git a/point.py b/point.py
new file mode 100644
index 0000000..829745b
--- /dev/null
+++ b/point.py
@@ -0,0 +1,28 @@
+
+
+
+class Point():
+
+    def __init__(self,x,y,mark=None):
+        self.x=x
+        self.y=y
+        self.mark=mark
+
+    def check_coincident(self, peer_p):
+
+        return (self.x == peer_p.x and self.y == peer_p.y and self.mark == peer_p.mark)
+
+    def shift_point(self, x_shift, y_shift):
+
+        self.x += x_shift
+        self.y += y_shift
+
+    def __eq__(self, other):
+        return self.x == other.x and self.y == other.y and self.mark == other.mark
+
+    def __str__(self):
+        return "x=%f,y=%f,mark=%s"%(self.x,self.y,self.mark)
+
+    def __add__(self, other):
+        return Point(self.x+other.x,self.y+other.y,self.mark)
+
diff --git a/point_pattern.ipynb b/point_pattern.ipynb
new file mode 100644
index 0000000..53d0182
--- /dev/null
+++ b/point_pattern.ipynb
@@ -0,0 +1,281 @@
+{
+ "metadata": {
+  "name": "point_pattern"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from point_pattern import PointPattern\n",
+      "from point  import Point\n",
+      "from utils import *\n",
+      "\n",
+      "import pysal as ps\n",
+      "\n",
+      "shapefile = ps.open(ps.examples.get_path('new_haven_merged.shp'))\n",
+      "dbf = ps.open(ps.examples.get_path('new_haven_merged.dbf'))\n",
+      "\n",
+      "point_list=[]\n",
+      "\n",
+      "for geometry, attributes in zip(shapefile, dbf):\n",
+      "    point_list.append(Point(geometry[0],geometry[1],attributes[0]))\n",
+      "\n",
+      "ptp=PointPattern(point_list)\n",
+      "\n",
+      "# Run a few tests to explore the dataset.\n",
+      "nn = ptp.average_nearest_neighbor_distance_KDTree()\n",
+      "print('This dataset has a nearest neighbor distance of {}'.format(nn))\n",
+      "# Use your monte carlo simulation code to see if the result is statistically significant\n",
+      "\n",
+      "smallest, largest = ptp.critical_points(ptp.permutations())\n",
+      "\n",
+      "if is_observed_distance_significant(smallest, largest, nn):\n",
+      "    print('The mark is significant.')\n",
+      "else:\n",
+      "    print('The mark is not significant.')\n",
+      "    \n",
+      "nn = ptp.average_nearest_neighbor_distance_KDTree(' Thu, Sept. 18th 2014')\n",
+      "print('This Thu, Sept. 18th 2014 has a nearest neighbor distance of {}'.format(nn))\n",
+      "# Use your monte carlo simulation code to see if the result is statistically significant\n",
+      "if is_observed_distance_significant(smallest, largest, nn):\n",
+      "    print('The mark is significant.')\n",
+      "else:\n",
+      "    print('The mark is not significant.')\n",
+      "\n",
+      "    \n",
+      "nn = ptp.average_nearest_neighbor_distance_KDTree(' Wed, Sept. 3rd 2014')\n",
+      "print('This Wed, Sept. 3rd 2014 has a nearest neighbor distance of {}'.format(nn))\n",
+      "# Use your monte carlo simulation code to see if the result is statistically significant\n",
+      "if is_observed_distance_significant(smallest, largest, nn):\n",
+      "    print('The mark is significant.')\n",
+      "else:\n",
+      "    print('The mark is not significant.')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "This dataset has a nearest neighbor distance of 0.000240361352928\n",
+        "The mark is not significant."
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "This Thu, Sept. 18th 2014 has a nearest neighbor distance of 0.00259760178027\n",
+        "The mark is significant.\n",
+        "This Wed, Sept. 3rd 2014 has a nearest neighbor distance of 0.00189031697659"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "The mark is significant.\n"
+       ]
+      }
+     ],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "compute_g = ptp.compute_g(10)\n",
+      "print(compute_g)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "0.111111111111\n"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "new_point_list=[ point for point in point_list if point.mark==' Thu, Sept. 18th 2014']\n",
+      "new_ptp=PointPattern(new_point_list)\n",
+      "compute_g = new_ptp.compute_g(10)\n",
+      "print(compute_g)\n",
+      "\n",
+      "new_point_list=[ point for point in point_list if point.mark==' Wed, Sept. 3rd 2014']\n",
+      "new_ptp=PointPattern(new_point_list)\n",
+      "compute_g = new_ptp.compute_g(10)\n",
+      "print(compute_g)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "0.111111111111\n",
+        "0.111111111111\n"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%pylab inline\n",
+      "import matplotlib.pyplot as plt\n",
+      "from point_pattern import PointPattern\n",
+      "from point  import Point\n",
+      "from utils import *\n",
+      "\n",
+      "import pysal as ps\n",
+      "\n",
+      "shapefile = ps.open(ps.examples.get_path('new_haven_merged.shp'))\n",
+      "dbf = ps.open(ps.examples.get_path('new_haven_merged.dbf'))\n",
+      "\n",
+      "point_list=[]\n",
+      "\n",
+      "for geometry, attributes in zip(shapefile, dbf):\n",
+      "    point_list.append(Point(geometry[0],geometry[1],attributes[0]))\n",
+      "\n",
+      "ptp=PointPattern(point_list)\n",
+      "\n",
+      "def plot_fun(x,y):\n",
+      "    \n",
+      "    plt.plot(x, y, 'g+')\n",
+      "\n",
+      "x=[point.x for point in ptp.points]\n",
+      "y=[point.y for point in ptp.points]\n",
+      "\n",
+      "plot_fun(x,y)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n",
+        "For more information, type 'help(pylab)'.\n"
+       ]
+      },
+      {
+       "output_type": "display_data",
+       "png": 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r4ht7v0FRXpEU5sKMOFbtK3G9ReVhYXsWAm44otSb+vpnvz7KFJeuAYl9rd6+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\ngyAIgugR/w8E3hxzzFk3OwAAAABJRU5ErkJggg==\n"
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "\n",
+      "plt.figure(1)\n",
+      "\n",
+      "compute_g_10 = ptp.compute_g(10)\n",
+      "compute_g_100 = ptp.compute_g(100)\n",
+      "compute_g_1000 = ptp.compute_g(1000)\n",
+      "plt.plot(0,compute_g,'ro')\n",
+      "plt.plot(0,compute_g_100,'ro')\n",
+      "plt.plot(0,compute_g_1000,'ro')\n",
+      "\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "display_data",
+       "png": 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+      }
+     ],
+     "prompt_number": 28
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from point_pattern import PointPattern\n",
+      "from point  import Point\n",
+      "from utils import *\n",
+      "%pylab inline\n",
+      "import matplotlib.pyplot as plt\n",
+      "import pysal as ps\n",
+      "\n",
+      "shapefile = ps.open(ps.examples.get_path('new_haven_merged.shp'))\n",
+      "dbf = ps.open(ps.examples.get_path('new_haven_merged.dbf'))\n",
+      "\n",
+      "point_list=[]\n",
+      "\n",
+      "for geometry, attributes in zip(shapefile, dbf):\n",
+      "    point_list.append(Point(geometry[0],geometry[1],attributes[0]))\n",
+      "\n",
+      "ptp=PointPattern(point_list)\n",
+      "\n",
+      "#Run a few tests to explore the dataset.\n",
+      "nn = ptp.average_nearest_neighbor_distance_KDTree()\n",
+      "\n",
+      "smallest, largest = ptp.critical_points(ptp.permutations(n=1000))\n",
+      "plt.figure(1)\n",
+      "\n",
+      "\n",
+      "plt.plot(0,nn,'ro')\n",
+      "plt.plot([-1,1],[smallest,smallest],'b+-')\n",
+      "plt.plot([-1,1],[largest,largest],'b+-')\n",
+      "\n",
+      "plt.axis([-1,1,0,largest+0.01])\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n",
+        "For more information, type 'help(pylab)'.\n"
+       ]
+      },
+      {
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAD5CAYAAADFqlkBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n"
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/point_pattern.py b/point_pattern.py
new file mode 100644
index 0000000..a503cf0
--- /dev/null
+++ b/point_pattern.py
@@ -0,0 +1,281 @@
+from point import Point
+import math
+import random
+import numpy as np
+import scipy.spatial as ss
+
+class PointPattern():
+
+    def __init__(self,points):
+        self.points = points[:]
+
+
+    def average_nearest_neighbor_distance(self,mark=None,points_list=None):
+
+        """
+        Given a set of points, compute the average nearest neighbor.
+
+        Parameters
+        ----------
+        points_list : list
+                 A list of points
+        mark   : str
+
+        Returns
+        -------
+        mean_d : float
+                 Average nearest neighbor distance
+
+        References
+        ----------
+        Clark and Evan (1954 Distance to Nearest Neighbor as a
+         Measure of Spatial Relationships in Populations. Ecology. 35(4)
+         p. 445-453.
+        """
+        mean_d = 0
+        if points_list==None:
+            points_tmp=self.points
+        else:
+	    points_tmp=points_list
+			
+	if mark!=None:
+            points_tmp=[ point for point in self.points if point.mark==mark]
+
+        length=len(points_tmp)
+
+        #print(self.points)
+        nearest_distances=[]
+        for i in range(length):
+            distance=[]
+            for j in range(length):
+                if i==j:
+                    continue
+                else:
+                    distance.append(self.euclidean_distance((points_tmp[i].x,points_tmp[i].y),(points_tmp[j].x,points_tmp[j].y)))
+            nearest_distances.append(min(distance))
+
+        mean_d=float(sum(nearest_distances)/len(nearest_distances))
+        return mean_d
+
+
+    def average_nearest_neighbor_distance_KDTree(self,mark=None):
+
+        """
+        Given a set of points, compute the average nearest neighbor.
+
+        Parameters
+        ----------
+        points : list
+                 A list of points
+        mark   : str
+
+        Returns
+        -------
+        mean_d : float
+                 Average nearest neighbor distance
+
+        References
+        ----------
+        Clark and Evan (1954 Distance to Nearest Neighbor as a
+         Measure of Spatial Relationships in Populations. Ecology. 35(4)
+         p. 445-453.
+        """
+
+        if mark==None:
+            points_tmp=self.points
+        else:
+            points_tmp=[ point for point in self.points if point.mark==mark]
+
+        nearest_distances=[]
+        point_list=[(point.x,point.y) for point in points_tmp]
+        point_stack = np.vstack(point_list)
+        kdtree = ss.KDTree(point_stack)
+
+        for p in point_stack:
+            nearest_neighbor_distance,nearest_neighbor = kdtree.query(p, k=2)
+            nearest_distances.append(nearest_neighbor_distance[1])
+
+        nn_distances = np.array(nearest_distances)
+
+        return np.mean(nn_distances)
+
+    def average_nearest_neighbor_distance_np(self,mark=None):
+
+        """
+        Given a set of points, compute the average nearest neighbor.
+
+        Parameters
+        ----------
+        points : list
+                 A list of points
+        mark   : str
+
+        Returns
+        -------
+        mean_d : float
+                 Average nearest neighbor distance
+
+        References
+        ----------
+        Clark and Evan (1954 Distance to Nearest Neighbor as a
+         Measure of Spatial Relationships in Populations. Ecology. 35(4)
+         p. 445-453.
+        """
+
+        if mark==None:
+            points_tmp=self.points
+        else:
+            points_tmp=[ point for point in self.points if point.mark==mark]
+
+        nearest_distances=[]
+        point_list=[(point.x,point.y) for point in points_tmp]
+        ndarray = np.array(point_list)
+
+        for i in range(len(ndarray)):
+            distance=[]
+            for j in range(len(ndarray)):
+                if i==j:
+                    continue
+                else:
+                    distance.append(ss.distance.euclidean(ndarray[i], ndarray[j]))
+            nearest_distances.append(np.min(distance))
+
+        return np.mean(nearest_distances)
+
+    def number_of_coincident_points(self):
+
+        length=len(self.points)
+        number_of_coincident_points=0
+        for i in range(length):
+            for j in range(length):
+                if i==j:
+                    continue
+                else:
+                    if self.check_coincident((self.points[i].x,self.points[i].y),(self.points[j].x,self.points[j].y)):
+                        number_of_coincident_points+=1
+        return number_of_coincident_points
+
+    def list_marks(self):
+
+        points_marks=set()
+        for point in self.points:
+            if point.mark!=None:
+                points_marks.add(point.mark)
+
+        return list(points_marks)
+
+    def list_subset_of_points(self,mark):
+
+        return [point for point in self.points if point.mark==mark]
+
+    def create_random_marked_points(self, n=None,domain_min=0,domain_max=1,marks=[]):
+
+        if n == None:
+            n=len(self.points)
+
+        randoms_list=np.random.uniform(domain_min, domain_max, (n,2))
+        points_list=[]
+        for i in range(n):
+            if len(marks)!=0:
+                points_list.append(Point(randoms_list[i][0], randoms_list[i][1],random.choice(marks)))
+            else:
+                points_list.append(Point(randoms_list[i][0], randoms_list[i][1]))
+        return points_list
+
+    def create_realizations(self, k):
+
+        return self.permutations(k)
+
+    def permutations(self,p=99, n=100):
+        """
+        Return the mean nearest neighbor distance of p permutations.
+
+        Parameters
+        ----------
+        p : integer
+        n : integer
+
+        Returns
+        -------
+        permutations : list
+                the mean nearest neighbor distance list.
+
+        """
+        permutation_list=[]
+        for i in range(p):
+            permutation_list.append(self.average_nearest_neighbor_distance(points_list=self.create_random_marked_points(n)))
+        return permutation_list
+
+    def critical_points(self,permutations):
+        """
+        Return the mean nearest neighbor distance of p permutations.
+
+        Parameters
+        ----------
+        permutations : list
+            the mean nearest neighbor distance list.
+        Returns
+        -------
+        smallest  : float
+        largest   : float
+
+        """
+
+        return min(permutations),max(permutations)
+
+    def compute_g(self,nsteps):
+
+        ds = np.linspace(0, 1, nsteps)
+        min_list=[]
+        for i_index,di in enumerate(ds):
+            tmp_list=[]
+            for j_index,dj in enumerate(ds):
+                if i_index != j_index:
+                    tmp_list.append(np.abs(di-dj))
+
+            min_list.append(np.min(tmp_list))
+
+        return np.mean(min_list)
+
+
+
+    def check_coincident(self,a, b):
+        """
+        Check whether two points are coincident
+        Parameters
+        ----------
+        a : tuple
+            A point in the form (x,y)
+
+        b : tuple
+            A point in the form (x,y)
+
+        Returns
+        -------
+        equal : bool
+                Whether the points are equal
+        """
+        return a == b
+
+    def euclidean_distance(self,a, b):
+        """
+        Compute the Euclidean distance between two points
+
+        Parameters
+        ----------
+        a : tuple
+            A point in the form (x,y)
+
+        b : tuple
+            A point in the form (x,y)
+
+        Returns
+        -------
+
+        distance : float
+                   The Euclidean distance between the two points
+        """
+        distance = math.sqrt((a[0] - b[0])**2 + (a[1] - b[1])**2)
+        return distance
+
+
diff --git a/utils.py b/utils.py
new file mode 100644
index 0000000..1a389b5
--- /dev/null
+++ b/utils.py
@@ -0,0 +1,246 @@
+import math
+import random
+from analytics import average_nearest_neighbor_distance
+from point import Point
+
+def generate_random_points(n):
+    """
+    Return n random points.
+
+    Parameters
+    ----------
+    n : integer
+
+    Returns
+    -------
+    points_list : list
+           The random points list
+
+    """
+    return [Point(random.uniform(0,1), random.uniform(0,1)) for i in range(n)]
+
+
+def create_random_marked_points(n, marks=[]):
+
+    points_list=[]
+    for i in range(n):
+        points_list.append(Point(random.uniform(0,1), random.uniform(0,1),random.choice(marks)))
+
+    return points_list
+
+
+
+def  permutation(p=99, n=100):
+    """
+    Return the mean nearest neighbor distance of p permutations.
+
+    Parameters
+    ----------
+    p : integer
+    n : integer
+
+    Returns
+    -------
+    permutations : list
+            the mean nearest neighbor distance list.
+
+    """
+    permutation_list=[]
+    for i in range(p):
+        permutation_list.append(average_nearest_neighbor_distance(generate_random_points(n)))
+    return permutation_list
+
+
+def  permutation_mark(p=99, n=100 ,marks=None,mark=None):
+    """
+    Return the mean nearest neighbor distance of p permutations.
+
+    Parameters
+    ----------
+    p : integer
+    n : integer
+    marks : list
+
+    Returns
+    -------
+    permutations : list
+            the mean nearest neighbor distance list.
+
+    """
+    permutation_list=[]
+    for i in range(p):
+        permutation_list.append(average_nearest_neighbor_distance(create_random_marked_points(n,marks),mark))
+    return permutation_list
+
+def  critical_points(permutations):
+    """
+    Return the mean nearest neighbor distance of p permutations.
+
+    Parameters
+    ----------
+    permutations : list
+        the mean nearest neighbor distance list.
+    Returns
+    -------
+    smallest  : float
+    largest   : float
+
+    """
+
+    return min(permutations),max(permutations)
+
+
+def  is_observed_distance_significant(smallest,largest,observed_distance):
+    """
+    Returns True is the observed distance is significant.
+
+    Parameters
+    ----------
+    smallest : float
+    largest  : float
+
+    Returns
+    -------
+    bool
+
+    """
+
+    if observed_distance>=smallest and observed_distance<=largest:
+        return  False
+    else:
+        return True
+
+
+
+
+def manhattan_distance(a, b):
+    """
+    Compute the Manhattan distance between two points
+
+    Parameters
+    ----------
+    a : tuple
+        A point in the form (x,y)
+
+    b : tuple
+        A point in the form (x,y)
+
+    Returns
+    -------
+    distance : float
+               The Manhattan distance between the two points
+    """
+    distance =  abs(a[0] - b[0]) + abs(a[1] - b[1])
+    return distance
+
+
+def shift_point(point, x_shift, y_shift):
+    """
+    Shift a point by some amount in the x and y directions
+
+    Parameters
+    ----------
+    point : tuple
+            in the form (x,y)
+
+    x_shift : int or float
+              distance to shift in the x direction
+
+    y_shift : int or float
+              distance to shift in the y direction
+
+    Returns
+    -------
+    new_x : int or float
+            shited x coordinate
+
+    new_y : int or float
+            shifted y coordinate
+
+    Note that the new_x new_y elements are returned as a tuple
+
+    Example
+    -------
+    >>> point = (0,0)
+    >>> shift_point(point, 1, 2)
+    (1,2)
+    """
+    x = getx(point)
+    y = gety(point)
+
+    x += x_shift
+    y += y_shift
+
+    return x, y
+
+
+def check_coincident(a, b):
+    """
+    Check whether two points are coincident
+    Parameters
+    ----------
+    a : tuple
+        A point in the form (x,y)
+
+    b : tuple
+        A point in the form (x,y)
+
+    Returns
+    -------
+    equal : bool
+            Whether the points are equal
+    """
+    return a == b
+
+
+def check_in(point, point_list):
+    """
+    Check whether point is in the point list
+
+    Parameters
+    ----------
+    point : tuple
+            In the form (x,y)
+
+    point_list : list
+                 in the form [point, point_1, point_2, ..., point_n]
+    """
+    return point in point_list
+
+
+def getx(point):
+    """
+    A simple method to return the x coordinate of
+     an tuple in the form(x,y).  We will look at
+     sequences in a coming lesson.
+
+    Parameters
+    ----------
+    point : tuple
+            in the form (x,y)
+
+    Returns
+    -------
+     : int or float
+       x coordinate
+    """
+    return point[0]
+
+
+def gety(point):
+    """
+    A simple method to return the x coordinate of
+     an tuple in the form(x,y).  We will look at
+     sequences in a coming lesson.
+
+    Parameters
+    ----------
+    point : tuple
+            in the form (x,y)
+
+    Returns
+    -------
+     : int or float
+       y coordinate
+    """
+    return point[1]
diff --git a/view.py b/view.py
new file mode 100644
index 0000000..083cb08
--- /dev/null
+++ b/view.py
@@ -0,0 +1,83 @@
+import sys
+from PyQt4 import QtGui
+
+
+class Assignment9(QtGui.QMainWindow):
+    
+    def __init__(self):
+        
+        super(Assignment9, self).__init__()
+        self.initUI()
+        
+        
+    
+    def initUI(self):               
+        
+        #create the exit aciton
+        exitAction = QtGui.QAction(QtGui.QIcon('exit.png'), '&Exit', self)        
+        exitAction.setShortcut('Ctrl+Q')
+        exitAction.setStatusTip('Exit application')
+        exitAction.triggered.connect((QtGui.qApp.quit))
+        
+        
+        #set statusBar
+        self.statusBar()
+        
+        #create the textEdit
+        textEdit = QtGui.QTextEdit()
+        self.setCentralWidget(textEdit)
+        
+        
+        #self.setCentralWidget(a_botton)
+        
+        menubar = self.menuBar()
+        fileMenu = menubar.addMenu('&File')
+        fileMenu.addAction(exitAction)
+        
+        self.setGeometry(300, 300, 300, 200)
+        
+        #set the titile
+        self.setWindowTitle('Assignment9')    
+        #make the window in the center
+        
+        self.center()
+        self.show()
+        
+        
+    def closeEvent(self, event):
+        """
+        
+        Show a message box when the user close the window.
+        """
+        
+        reply = QtGui.QMessageBox.question(self, 'Message',
+            "Are you sure to quit?", QtGui.QMessageBox.Yes | 
+            QtGui.QMessageBox.No, QtGui.QMessageBox.No)
+
+        if reply == QtGui.QMessageBox.Yes:
+            event.accept()
+        else:
+            event.ignore()        
+        
+    def center(self):
+        """
+        
+        Set the window on the center of deskp.
+        """
+        
+        qr = self.frameGeometry()
+        cp = QtGui.QDesktopWidget().availableGeometry().center()
+        qr.moveCenter(cp)
+        self.move(qr.topLeft())
+        
+        
+        
+def main():
+    
+    app = QtGui.QApplication(sys.argv)
+    ex = Assignment9()
+    sys.exit(app.exec_())
+
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file