Initial push

PointPattern.py

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+from .point import Point
+from . import analytics
+import random
+import numpy as np
+
+def PointPattern(object):
+    def __init__(self):
+        self.points = []
+
+    def average_nearest_neighbor_distance(self, mark=None):
+        return analytics.average_nearest_neighbor_distance(self.points, mark)
+
+    def add_point(self, point):
+        self.points.append(point)
+
+    def remove_point(self, index):
+        del(self.points[index])
+
+    def count_coincident_points(self):
+        count = 0
+        coincidnet_list = []
+
+        for i, point in enumerate(self.points):
+            for j, point2 in enumerate(self.points):
+                if i is j:
+                    continue
+                if j in coincident_list:
+                    continue
+                #Should use the magic method in point class
+                if point == point2:
+                    count += 1
+                    coincident_list.append(j)
+        return count
+
+    def list_marks(self):
+        marks = []
+
+        for point in self.points:
+            if point.mark is not None and point.mark not in marks:
+                marks.append(point.mark)
+
+    def return_subset(self, mark):
+        #creates a list of points that have the same mark as passed
+        return [i for i in self.points if i == mark]
+
+    def create_random_points(self, n=None):
+        rand_points = []
+        rand = random.Random()
+        marks = ['North', 'East', 'South', 'West']
+
+        if n is None:
+            n = len(self.points)
+
+        for i in range(n):
+            rand_points.append(point.Point(rand.randint(1,100), rand.randint(1,100), mark=rand.choice(marks)))
+
+        return rand_points
+
+    def create_realizations(self, k):
+        return analytics.permutations(k)
+
+    def critical_points(self):
+        return analytics.find_criticals(self.create_realizations(99))
+
+    def compute_g(self, nsteps):
+        ds = np.linspace(0, 100, nsteps)
+        g_sum = 0
+
+        for step in range(nsteps):
+            o_i = ds[step]
+            min_dis = None
+            for i, j in enumerate(ds):
+
+                temp = abs(j - o_i)
+
+                if i is step:
+                    continue
+                if min_dis is None:
+                    min_dis = temp
+                elif min_dis > temp:
+                    min_dis = temp
+                else:
+                    continue
+            g_sum += min_dis
+        return g_sum / nsteps
+
+
+

analytics.py

@@ -0,0 +1,274 @@
+#analytics
+import math
+import json
+import sys 
+import os
+
+sys.path.insert(0, os.path.abspath('..'))
+
+from . import utils
+from . import point
+
+def find_largest_city(gj):
+    """
+    Iterate through a geojson feature collection and
+    find the largest city.  Assume that the key
+    to access the maximum population is 'pop_max'.
+
+    Parameters
+    ----------
+    gj : dict
+         A GeoJSON file read in as a Python dictionary
+
+    Returns
+    -------
+    city : str
+           The largest city
+
+    population : int
+                 The population of the largest city
+    """
+    temp = gj['features']
+    city = ""
+    max_population = 0
+
+    for i in temp:
+        if (i['properties']['pop_max'] > max_population):
+            max_population = i['properties']['pop_max']
+            city = i['properties']['name']
+
+    return city, max_population
+
+def write_your_own(gj):
+    """
+    Here you will write your own code to find
+    some attribute in the supplied geojson file.
+
+    Take a look at the attributes available and pick
+    something interesting that you might like to find
+    or summarize.  This is totally up to you.
+
+    Do not forget to write the accompanying test in
+    tests.py!
+    """
+    #Finds how many megacities there are in the geoJSON
+    temp = gj['features']
+    megacities = 0
+
+    for i in temp:
+        if(i['properties']['megacity'] == 1):
+            megacities += 1
+
+
+    return megacities
+
+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 = 0
+    y = 0
+
+    for point in points:
+        x += point[0]
+        y += point[1] 
+
+    x = x / len(points)
+    y = y / len(points)
+
+    return x, y
+
+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 in the form (x,y)
+
+    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.
+    """
+
+    temp_points = []
+
+    if mark is not None:
+        for point in points:
+            if point.mark is mark:
+                temp_points.append(point)
+    else:
+        temp_points = points
+
+    nearest = []
+
+    for i, point in enumerate(temp_points):
+        nearest.append(None)
+        for j, point2 in enumerate(temp_points):
+            if i is not j:
+                dist = euclidean_distance((point.x, point.y), (point2.x, point2.y))
+                if nearest[i] == None:
+                    nearest[i] = dist
+                elif nearest[i] > dist:
+                    nearest[i] = dist
+
+    mean_d = sum(nearest) / len(points)
+
+    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]
+    """
+
+    first = True 
+    mbr = [0,0,0,0]
+
+    for point in points:
+        if first:
+            first = False
+            mbr[0] = point[0]
+            mbr[1] = point[1]
+            mbr[2] = point[0]
+            mbr[3] = point[1]
+
+        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
+
+def mbr_area(mbr):
+    """
+    Compute the area of a minimum bounding rectangle
+    """
+    area = (mbr[1] - mbr[3]) * (mbr[0] - mbr[2])
+    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 = 0.5 * (area / n) ** 0.5
+    return expected
+
+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 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 permutations(p=99, n=100, marks=None):
+    perms = []
+
+    if marks is None:
+        for i in range(p):
+            perms.append(average_nearest_neighbor_distance(utils.create_random_marked_points(n, marks=None)))
+
+    else:
+        for i in range(p):
+            perms.append(average_nearest_neighbor_distance(utils.create_random_marked_points(n, marks)))
+
+    return perms
+
+def find_criticals(perms):
+    lower = min(perms)
+    upper = max(perms)
+    return lower, upper
+
+def check_significance(lower, upper, observed):
+    if observed > upper:
+        return True
+    elif observed < lower:
+        return True
+    else:
+        return False

io_geojson.py

@@ -0,0 +1,22 @@
+#io_geojson
+import json 
+
+def read_geojson(input_file):
+    """
+    Read a geojson file
+
+    Parameters
+    ----------
+    input_file : str
+                 The PATH to the data to be read
+
+    Returns
+    -------
+    gj : dict
+         An in memory version of the geojson
+    """
+    # Please use the python json module (imported above)
+    # to solve this one.
+    with open(input_file, 'r') as f:
+        gj = json.load(f)
+    return gj