Implemented algorithms

point_pattern.py

@@ -33,7 +33,8 @@ def read_geojson(input_file):
     """
     # Please use the python json module (imported above)
     # to solve this one.
-    gj = None
+    with open(input_file, 'r') as fp:
+        gj = json.load(fp)
     return gj
 
 
@@ -58,6 +59,11 @@ def find_largest_city(gj):
     """
     city = None
     max_population = 0
+    for item in gj["features"]:
+        props = item["properties"]
+        if (props["pop_max"] > max_population):
+            max_population = props["pop_max"]
+            city = props["adm1name"]
 
     return city, max_population
 
@@ -74,7 +80,15 @@ def write_your_own(gj):
     Do not forget to write the accompanying test in
     tests.py!
     """
-    return
+    # Finds the city with the greatest change in population (i.e. between pop_max and pop-min.
+    city = None
+    greatest_change = 0
+    for item in gj["features"]:
+        props = item["properties"]
+        if props["pop_max"] - props["pop_min"] > greatest_change:
+            greatest_change = props["pop_max"] - props["pop_min"]
+            city = props["adm1name"]
+    return city, greatest_change
 
 def mean_center(points):
     """
@@ -93,8 +107,16 @@ def mean_center(points):
     y : float
         Mean y coordinate
     """
-    x = None
-    y = None
+    x = 0
+    y = 0
+    n = 0
+    for point in points:
+        x += point[0]
+        y += point[1]
+        n += 1
+
+    x /= n
+    y /= n
 
     return x, y
 
@@ -120,6 +142,28 @@ def average_nearest_neighbor_distance(points):
      p. 445-453.
     """
     mean_d = 0
+    temp_nearest_neighbor = None
+    # Average the nearest neighbor distance of all points.
+    for point in points:
+        # Find the nearest neighbor to this point.
+        for otherPoint in points:
+            # You are not your own neighbor.
+            if check_coincident(point, otherPoint):
+                continue
+            # To avoid multiple calculations, we'll cache the result.
+            current_distance = euclidean_distance(point, otherPoint)
+            # nearest neighbor will be None if this is the first neighbor we have iterated over.
+            if temp_nearest_neighbor is None:
+                temp_nearest_neighbor = current_distance
+            elif temp_nearest_neighbor > current_distance:
+                temp_nearest_neighbor = current_distance
+        # At this point, we've found point's nearest neighbor distance.
+        # Add in that distance.
+        mean_d += temp_nearest_neighbor
+        temp_nearest_neighbor = None
+
+    # Divide by number of points.
+    mean_d /= len(points)
 
     return mean_d
 
@@ -139,7 +183,25 @@ def minimum_bounding_rectangle(points):
        Corners of the MBR in the form [xmin, ymin, xmax, ymax]
     """
 
-    mbr = [0,0,0,0]
+    mbr = [None,None,None,None]
+    for point in points:
+        # First iteration, everything is None. The point will
+        # form the initial boundaries for the rectangle.
+        if mbr[0] is None:
+            mbr[0] = point[0]
+            mbr[1] = point[1]
+            mbr[2] = point[0]
+            mbr[3] = point[1]
+        else:
+            # Verify that each edge is far enough. If not, extend the rectangle.
+            if point[0] < mbr[0]:
+                mbr[0] = point[0]
+            if point[1] < mbr[1]:
+                mbr[1] = point[1]
+            if point[0] > mbr[2]:
+                mbr[2] = point[0]
+            if point[1] > mbr[3]:
+                mbr[3] = point[1]
 
     return mbr
 
@@ -148,7 +210,7 @@ def mbr_area(mbr):
     """
     Compute the area of a minimum bounding rectangle
     """
-    area = 0
+    area = (mbr[3] - mbr[1]) * (mbr[2] - mbr[0])
 
     return area
 
@@ -173,7 +235,7 @@ def expected_distance(area, n):
         The number of points
     """
 
-    expected = 0
+    expected = 0.5 * ((area / n) ** 0.5)
     return expected
 
 

tests/tests.py

@@ -32,8 +32,9 @@ def test_write_your_own(self):
         Here you will write a test for the code you write in
         point_pattern.py.
         """
-        some_return = point_pattern.write_your_own(self.gj)
-        self.assertTrue(False)
+        city, pop = point_pattern.write_your_own(self.gj)
+        self.assertEqual(city, "New York")
+        self.assertEqual(pop, 11031722)
 
 class TestIterablePointPattern(unittest.TestCase):
     """