initial commit with import error

analytics.py

@@ -0,0 +1,289 @@
+import math
+import sys
+import os
+import random
+#from utils import euclidean_distance, n_random_Points
+#sys.path.insert(0, os.path.abspath('..'))
+
+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
+    """
+    #features is a list, so iteration is by position
+    #if you want to iterate over the features you need to first grab the list out of the dictionary.
+
+    featureList = gj['features']
+    # now that you have the features, compare the pop_max fields to find the largest one
+    max_population = 0
+    for featureEntry in featureList:
+        if featureEntry["properties"]["pop_max"] > max_population:
+            max_population = featureEntry["properties"]["pop_max"]
+            city = featureEntry["properties"]["nameascii"]
+
+
+    return city, max_population
+
+def write_your_own(gj):
+    """
+    This function finds the least populated city, pop_min
+    """
+    featureList = gj["features"]
+    minPop = math.inf
+    for featureEntry in featureList:
+        #feature["properties"]["pop_min"] for feature in self.gj["features"]
+        if featureEntry["properties"]["pop_min"] < minPop:
+            minPop = featureEntry["properties"]["pop_min"]
+            city = featureEntry["properties"]["nameascii"]
+  #  minn = min(featureEntry["properties"]["pop_min"])
+#    print(minn)
+    return city, minPop
+
+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
+    """
+
+    #find the average of all the X points in the list
+
+  #  x_sum = sum(points[0])
+    #points_length = len(points)
+
+    sums = map(sum,zip(*points)) # returns iterable object of type map
+    sumsL = list(sums)
+    avgs = map(lambda xy: xy/len(points),sumsL)
+    avgsL = list(avgs)
+    x = avgsL[0]
+    y = avgsL[1]
+
+    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 of (x,y,mark) or points with (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.
+    """
+    markList = []
+    if not mark: #If mark is empty, then you're computing the distance of all the points
+        shDistL =[]       #list of shortest distances
+
+        #now the points are numbered... so if the points
+        #have the same counter number attached also, then they
+        #are self-neighbors, but if num1 != num2, then they are
+        # coincident points, with distance = 0
+        printXonce = False
+        for num1, point in enumerate(points):
+            shortestDistance = math.inf
+            for num2, dpoint in enumerate(points):
+                if num1 != num2:
+                    if printXonce == False:
+                        print(point.x)
+                    epoint1 = (point.x,point.y)
+                    epoint2 = (dpoint.x,dpoint.y)
+                    dist = utils.euclidean_distance(epoint1, epoint2)  #changed input parameters because cannot pass in Point
+                    if(shortestDistance > dist):
+                        shortestDistance = dist
+                printXonce = True
+            #now add the shortest distance of that point before it moves on to a new point
+            shDistL.append(shortestDistance)
+       # print(shDistL)
+        sums = sum(shDistL)
+        mean_d = sums/len(shDistL)
+    #compute the average nearest neighbor distance of only those that share the mark
+    else:
+        for p in points:
+            if p.mark in mark:  #passed in a list of possible marks
+                markList.append(p)
+        shDistL =[]       #list of shortest distances
+
+        #now the points are numbered... so if the points
+        #have the same counter number attached also, then they
+        #are self-neighbors, but if num1 != num2, then they are
+        # coincident points, with distance = 0
+        for num1, point in enumerate(markList):
+            shortestDistance = math.inf
+            for num2, dpoint in enumerate(markList):
+                if num1 != num2:
+                    dist = utils.euclidean_distance((point.x,point.y), (dpoint.x,dpoint.y))
+                    if(shortestDistance > dist):
+                        shortestDistance = dist
+            #now add the shortest distance of that point before it moves on to a new point
+            shDistL.append(shortestDistance)
+        #print(shDistL)
+        sums = sum(shDistL)
+        mean_d = sums/len(shDistL)
+        print(mean_d)
+    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]
+    """
+    # a minimum bounding rectangle would be on the extremes of x/y
+
+    xmin = math.inf
+    ymin = math.inf
+    xmax = -9999999999
+    ymax = -9999999999
+    for point in points:
+        if point[0] < xmin:
+            xmin = point[0]
+        if point[1] < ymin:
+            ymin = point[1]
+        if point[0] > xmax:
+            xmax = point[0]
+        if point[1] > ymax:
+            ymax = point[1]
+    mbr = [xmin,ymin,xmax,ymax]
+    print("This is the mbr:")
+    print(mbr)
+    return mbr
+
+def mbr_area(mbr):
+    """
+    Compute the area of a minimum bounding rectangle
+    """
+    length = mbr[2] - mbr[0]
+    width  = mbr[3] - mbr[1]
+    area = length*width
+
+    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 * (math.sqrt(area/n))
+    return expected
+
+
+def permutation_nearest_distance(mark=[],p=99,n=100):
+    """
+    Finds the nearest neighbor distance for p permutations with n
+    random points
+    :param p: permutation number of times you want to try different
+    simulations for monte carlo
+    :param n: random point number
+    :param mark: Passes in a list of marks if the permutation to be found is of Points
+    :return LDist: list of distances, length p
+    """
+ #   if mark == None:
+ #       LDist = []
+ #       for x in range(p): #loop from 0 to p
+ #           #create n random Points
+ #           points = n_random_points(n) # returns [(x,y),(a,b)..]
+ #           #compute mean neighbor distance
+#            mean_d = average_nearest_neighbor_distance(points)
+ #           LDist.append(mean_d)
+
+    LDist = []
+    for x in range(p): #loop from 0 to p
+        #create n random Points
+        points = utils.n_random_Points(n,mark) # returns [(x,y),(a,b)..]
+        print("print the points array: ")
+        print(points)
+        print(type(points))
+        #compute m ean neighbor distance
+        mean_d = average_nearest_neighbor_distance(points,mark)
+        LDist.append(mean_d)
+
+    return LDist
+
+def critical_points(LDist):
+    """
+    Find the critical points, the largest/smallest distances
+    :param LDist: the list of mean distances
+    :return CList: list containing critical points
+    """
+    CList = []
+    smallest = min(LDist)
+    largest = max(LDist)
+    CList.append(smallest)
+    CList.append(largest)
+    #print(CList)
+    return CList
+
+def significant(CList,distance):
+    """
+    Returns True if the observed distance is significant
+    :param CList: list of critical points
+    :param distance: the observed distance
+    :return result: True/False
+    """
+
+    if distance < CList[0] or distance > CList[1]:
+        result = True
+    else:
+        result = False
+    return result
+
+from . import utils

point.py

@@ -0,0 +1,24 @@
+import unittest
+import sys
+import os
+#sys.path.insert(0, os.path.abspath('..'))
+
+
+class Point(object):
+    def __init__(self,x,y,mark={}):
+        self.x = x
+        self.y = y
+        self.mark = mark
+
+    def patched_coincident(self,point2):
+        point1 = (self.x,self.y)
+
+        return utils.check_coincident(point1,point2)
+
+    def patched_shift(self,x_shift,y_shift):
+        point = (self.x,self.y)
+        self.x,self.y = utils.shift_point(point,x_shift,y_shift)
+
+
+#put import statement at the end to avoid cyclic dependancy
+from . import utils