diff --git a/.idea/.name b/.idea/.name
new file mode 100644
index 0000000..ccf6ee9
--- /dev/null
+++ b/.idea/.name
@@ -0,0 +1 @@
+assignment_10
\ No newline at end of file
diff --git a/.idea/assignment_10.iml b/.idea/assignment_10.iml
new file mode 100644
index 0000000..6f63a63
--- /dev/null
+++ b/.idea/assignment_10.iml
@@ -0,0 +1,12 @@
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\ No newline at end of file
diff --git a/.idea/encodings.xml b/.idea/encodings.xml
new file mode 100644
index 0000000..97626ba
--- /dev/null
+++ b/.idea/encodings.xml
@@ -0,0 +1,6 @@
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\ No newline at end of file
diff --git a/.idea/misc.xml b/.idea/misc.xml
new file mode 100644
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--- /dev/null
+++ b/.idea/misc.xml
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\ No newline at end of file
diff --git a/.idea/modules.xml b/.idea/modules.xml
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--- /dev/null
+++ b/.idea/modules.xml
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\ No newline at end of file
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
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diff --git a/.idea/workspace.xml b/.idea/workspace.xml
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diff --git a/Assignment 10.ipynb b/Assignment 10.ipynb
new file mode 100644
index 0000000..acb1654
--- /dev/null
+++ b/Assignment 10.ipynb
@@ -0,0 +1,620 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "import io_geojson as iogj"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "tweets = iogj.process_tweets(\"tweets.json\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
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+ "key: 716472558040657920\n",
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+ "key: 716473382817017856\n",
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+ "key: 716471073370279936\n",
+ "key: 716471070124060672\n",
+ "key: 716472402184523776\n",
+ "key: 716472327072919552\n",
+ "key: 716471941654192128\n",
+ "key: 716473049286066176\n",
+ "key: 716471757415145472\n",
+ "key: 716470681781665792\n",
+ "key: 716471981445545984\n",
+ "key: 716470691894312960\n",
+ "key: 716472274010787840\n",
+ "key: 716471407761186816\n",
+ "key: 716472101880737792\n",
+ "key: 716470930780753920\n",
+ "key: 716472333859291136\n",
+ "key: 716473212574404608\n",
+ "key: 716472408006209536\n",
+ "key: 716472179689283584\n",
+ "key: 716472510384963584\n",
+ "key: 716471019821600769\n",
+ "key: 716470485836386304\n",
+ "key: 716470880906428416\n",
+ "key: 716471170845921281\n",
+ "key: 716470411291013120\n",
+ "key: 716472218872455169\n",
+ "key: 716472496967393281\n",
+ "key: 716473204752191488\n",
+ "key: 716472406005579776\n",
+ "key: 716473090461437952\n",
+ "key: 716472659500859392\n",
+ "key: 716471148146364417\n",
+ "key: 716470648227254273\n",
+ "key: 716471835194302467\n",
+ "key: 716470626664382464\n",
+ "key: 716471877149937664\n",
+ "key: 716470584859697152\n",
+ "key: 716471863421972481\n",
+ "key: 716470870516994048\n",
+ "key: 716471451348508672\n",
+ "key: 716472046595616769\n",
+ "key: 716473693258575872\n",
+ "key: 716471278278840320\n",
+ "key: 716470887050940416\n",
+ "key: 716472030581751808\n",
+ "key: 716471709189079040\n",
+ "key: 716470882844028928\n",
+ "key: 716471462232633344\n"
+ ]
+ }
+ ],
+ "source": [
+ "for key in tweets:\n",
+ " print (\"key: %s\" % (key))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "\"it's pass 8 and I still haven't eaten dinner yet.... AGAIN⁉️‼️\""
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tweets[716470349609603072]['text']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0\n",
+ "1\n",
+ "2\n",
+ "3\n"
+ ]
+ }
+ ],
+ "source": [
+ "for i in range(4):\n",
+ " print (i)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.5.1"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/io_geojson.py b/io_geojson.py
new file mode 100644
index 0000000..1e299c9
--- /dev/null
+++ b/io_geojson.py
@@ -0,0 +1,100 @@
+import json # for the geojson stuff
+
+# ------------------------------------------------------------------
+#
+# Joseph Cruz
+# io_geojson.py
+#
+# Stores the functions from point_pattern.py that are a form of
+# utility needed for use in other functions
+# ------------------------------------------------------------------
+
+
+def process_tweets(input_file):
+ tweets_gj = read_geojson(input_file)
+ tweets_out = 0
+
+ list_tweet_ids = []
+ for j in range(len(tweets_gj)):
+ list_tweet_ids.append(tweets_gj[j]['id'])
+
+ tweets_out = {}
+ for i in range(len(list_tweet_ids)):
+ id_in = tweets_gj[i]['id']
+ tweets_out[id_in] = tweets_gj[i]
+
+ return tweets_out
+
+
+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) as iFile:
+ gj = json.load(iFile)
+ return gj
+
+
+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
+ """
+ gj_features = gj['features']
+ city = None
+ max_population = 0
+
+ for nextFeature in gj_features:
+ if nextFeature['pop_max'] > max_population:
+ city = nextFeature['name']
+ max_population = nextFeature['pop_max']
+
+ 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!
+ """
+ #Counts the number of world cities
+ gj_features = gj['features']
+ numWorldCities = 0
+
+ for nextFeature in gj_features:
+ if nextFeature['worldcity'] != 0:
+ numWorldCities += 1
+ return numWorldCities
diff --git a/point.py b/point.py
new file mode 100644
index 0000000..5842ec4
--- /dev/null
+++ b/point.py
@@ -0,0 +1,430 @@
+import random
+import math
+import numpy as np
+import scipy.spatial as ss
+import pysal as ps
+import io_geojson as iogj
+import folium
+
+
+# [Joseph] I decided to have Tweet incorporate a Point class
+# I can avoid inheritance and just use point data and methods
+# as if it were an attribute of Tweet.
+class Tweet:
+ def __init__(self):
+ self.point_data = Point()
+ self.text = ""
+ self.latitutde = 0.0
+ self.longitutde = 0.0
+ self.interesting = []
+
+
+class TweetPattern:
+ def __init__(self):
+ self.Tweets = []
+
+ def assign_lat_long(self, input_file):
+ set_tweets = iogj.process_tweets(input_file)
+ new_tweet = Tweet()
+
+ for key in set_tweets:
+ in_text = set_tweets[key]['text']
+
+ lat_min = 999
+ lat_max = -999
+ long_min = 999
+ long_max = -999
+
+ for i in range(4):
+ if i == 0:
+ long_min = set_tweets[key]['place']['bounding_box']['coordinates'][0][i][0]
+ long_max = set_tweets[key]['place']['bounding_box']['coordinates'][0][i][0]
+
+ lat_min = set_tweets[key]['place']['bounding_box']['coordinates'][0][i][1]
+ lat_max = set_tweets[key]['place']['bounding_box']['coordinates'][0][i][1]
+ else:
+ if set_tweets[key]['place']['bounding_box']['coordinates'][0][i][0] > long_max:
+ long_max = set_tweets[key]['place']['bounding_box']['coordinates'][0][i][0]
+ if set_tweets[key]['place']['bounding_box']['coordinates'][0][i][0] < long_min:
+ long_min = set_tweets[key]['place']['bounding_box']['coordinates'][0][i][0]
+
+ if set_tweets[key]['place']['bounding_box']['coordinates'][0][i][1] > lat_max:
+ lat_max = set_tweets[key]['place']['bounding_box']['coordinates'][0][i][1]
+ if set_tweets[key]['place']['bounding_box']['coordinates'][0][i][1] < lat_min:
+ lat_min = set_tweets[key]['place']['bounding_box']['coordinates'][0][i][1]
+
+ x = random.uniform(long_min, long_max)
+ y = random.uniform(lat_min,lat_max)
+
+ new_tweet.latitude = y
+ new_tweet.longitutde = x
+ new_tweet.text = in_text
+ self.Tweets.append(new_tweet)
+
+
+ def build_folium_map(self):
+ map_tweets = folium.map(location=[33.42,-112.065])
+ for j in range(len(self.Tweets)):
+ folium.marker([self.Tweets[j].y,self.Tweets[j].x],popup = self.Tweets[j].text)
+
+class Point:
+ """
+ For whatever reason, cannot get import to work, just moved functionality
+ into this file instead.
+ """
+ def __init__(self,x = 0,y = 0,mark = ""):
+ self.x = x
+ self.y = y
+ self.mark = mark
+
+ def __eq__(self, other):
+ return self.x == other.x and self.y == other.y
+
+ def __gt__(self, other):
+ direction = ""
+ if self.y > other.y:
+ direction += "N"
+ elif self.y == other.y:
+ direction += "-"
+ else:
+ direction += "S"
+ if self.x > other.x:
+ direction += "E"
+ elif self.x == other.y:
+ direction += "-"
+ else:
+ direction += "W"
+
+ return direction
+
+ def __neg__(self):
+ return Point(-self.x,-self.y,self.mark)
+
+ def shift_point(self,x_move,y_move):
+ this_point = (self.x,self.y)
+ self.x += x_move
+ self.y += y_move
+
+ """
+ Made redundant by __eq__
+ def coincident(self, check_point):
+ if check_point.x == self.x and check_point.y == self.y:
+ return True
+ else:
+ return False
+ """
+
+
+class PointPattern:
+ def __init__(self):
+ self.set_of_points = []
+
+ def avg_nearest_neighbor_dist(self,mark):
+ average_nearest_neighbor_distance(self.set_of_points, mark)
+
+ def num_coincident_points(self):
+ coincident_points = 0
+ len_list = len(self.set_of_points)
+ for i in range(len_list):
+ if i == len_list-1:
+ break
+ for j in range(len_list+1):
+ if self.set_of_points[i] == self.set_of_points[j]:
+ coincident_points += 1
+
+ return coincident_points
+
+ def list_marks(self):
+ mark_list = []
+ len_list = len(self.set_of_points)
+ for i in range(len_list):
+ if not mark_list:
+ mark_list.append(self.set_of_points[i].mark)
+ elif self.set_of_points[i].mark not in mark_list:
+ mark_list.append(self.set_of_points[i].mark)
+
+ return mark_list
+
+ def list_mark_subsets(self):
+ mark_list = PointPattern.list_marks(self)
+ mark_count = []
+ sub_set = []
+ for i in range(len(mark_list)):
+ sub_set = []
+ for j in range(len(self.set_of_points)):
+ if mark_list[i] == self.set_of_points[j].mark:
+ sub_set.append(self.set_of_points[j])
+ mark_count.append(sub_set)
+
+ return mark_count
+
+ def create_random_points(self, n=100, marks = []):
+ if not marks:
+ self.set_of_points = create_random_unmarked_points(n)
+ else:
+ self.set_of_points = create_random_marked_points(n,marks)
+
+ def k_realizations(self,k = 99):
+ return permutations(k,n = 100)
+
+ def critical_points(self,realizations):
+ return find_crit_points(realizations)
+
+ def compute_g(self, min_dist):
+ gs = 0
+ len_list = len(self.set_of_points)
+ all_pts_min_dists = []
+ for i in range(len_list):
+ local_nn = 0
+ for j in range(len_list):
+ if i != j:
+ new_distance = euclidean_distance(self.set_of_points[i],self.set_of_points[j])
+ if local_nn == 0:
+ local_nn = new_distance
+ elif new_distance < local_nn:
+ local_nn = new_distance
+
+ all_pts_min_dists.append(local_nn)
+
+ for k in range(len(all_pts_min_dists)):
+ if all_pts_min_dists[k]<=min_dist:
+ gs += 1
+
+ return gs
+ """
+ Assignment 8 stuff
+ """
+
+ def nearest_neighbor_kdtree(self, mark=""):
+ buffer = build_np_array(self.set_of_points, mark)
+ kdtree = ss.KDTree(buffer)
+ calc_distances = []
+ values = []
+ for i in buffer:
+ nn_distance, nn = kdtree.query(i, k=2)
+ #do we remove the first one since it is the coincident?
+ calc_distances.append(nn_distance[1])
+ values.append(nn)
+ distances = np.array(calc_distances)
+ return distances
+
+ def nn_kdtree_avg(self, mark=""):
+ return np.mean(self.nearest_neighbor_kdtree(mark))
+
+ def np_compute_g(self, min_dist, mark=""):
+ buffer = build_np_array(self.set_of_points, mark)
+ g = 0
+ n = len(self.set_of_points)
+ list_of_min_dists = []
+ for i in range(len(self.set_of_points)):
+ local_nn = 0
+ for j in range(len(self.set_of_points)):
+ if i != j:
+ new_dist = ss.distance.euclidean(buffer[i],buffer[j])
+ if local_nn == 0:
+ local_nn = new_dist
+ elif new_dist < local_nn:
+ local_nn = new_dist
+ list_of_min_dists.append(local_nn)
+
+ for k in range(len(list_of_min_dists)):
+ if list_of_min_dists[k] <= min_dist:
+ g += 1
+
+ return g/n
+
+ def np_gen_random_points(self, n = 100, dom_spec = False):
+ lop = []
+ if dom_spec:
+ lx,ly,mx,my = minimum_bounding_rectangle(self.set_of_points)
+ for i in range(n):
+ x = np.random.uniform(lx, mx)
+ y = np.random.uniform(ly, my)
+ n_p = Point(x,y)
+ lop.append(n_p)
+ else:
+ for i in range(n):
+ tuples = np.random.uniform(0,1,(1,2))
+ n_p = Point(tuples[0][0],tuples[0][1])
+ lop.append(n_p)
+
+ return lop
+
+
+"""
+Assignment_08
+"""
+
+
+def build_np_array(points, mark=""):
+ if not points:
+ return "No Points in List"
+ point_buffer = []
+
+ if mark == "":
+ for i in range(len(points)):
+ new_tuple = [points[i].x,points[i].y]
+ point_buffer.append(new_tuple)
+
+ else:
+ for i in range(len(points)):
+ if points[i].mark == mark:
+ new_tuple = [points[i].x,points[i].y]
+ point_buffer.append(new_tuple)
+
+ return np.array(point_buffer)
+
+
+def nn_kdtree(points):
+ tree = ss.KDTree(points)
+ distances = []
+ values = []
+ for i in points:
+ nn_distance, nn = tree.query(i, k=2)
+ distances.append(nn_distance)
+ values.append(nn)
+
+ return distances,values
+
+
+def np_create_rand_points(self, n, specified):
+ lop = []
+ if not specified:
+ for i in range(n):
+ tup = np.random.uniform(0,1,(1,2))
+ point_n = Point(tup[0],tup[1])
+ lop.append(point_n)
+ else:
+ minimum_bounding_rectangle(self.set_of_points)
+
+ return lop
+
+
+"""
+Old Assignment
+"""
+
+
+def create_random_marked_points(n, marks = []):
+ list_of_tuples = [(random.uniform(0,1), random.uniform(0,1)) for i in range(n)]
+ list_of_marks = [random.choice(marks) for i in range(n)]
+ list_of_points = []
+ for j in range(n):
+ new_point = Point(list_of_tuples[j][0],list_of_tuples[j][1],list_of_marks[j])
+ list_of_points.append(new_point)
+
+ return list_of_points
+
+
+def create_random_unmarked_points(n):
+ list_of_tuples = [(random.uniform(0,1), random.uniform(0,1)) for i in range(n)]
+ list_of_points = []
+ for j in range(n):
+ new_point = Point(list_of_tuples[j][0],list_of_tuples[j][1], "")
+ list_of_points.append(new_point)
+
+ return list_of_points
+
+
+def euclidean_distance(a, b):
+ distance = math.sqrt((a.x - b.x)**2 + (a.y - b.y)**2)
+ return distance
+
+
+def average_nearest_neighbor_distance(points, mark=""):
+ mean_d = 0
+ total = 0
+ local_nn = 0
+ num_of_points = len(points)
+
+ if not mark:
+ for i in range(num_of_points):
+ local_nn = 0
+ for j in range(num_of_points):
+ if i != j:
+ new_distance = euclidean_distance(points[i],points[j])
+ if local_nn == 0:
+ local_nn = new_distance
+ elif new_distance < local_nn:
+ local_nn = new_distance
+
+ total += local_nn
+
+ else:
+ for i in range(num_of_points):
+ local_nn = 0
+ for j in range(num_of_points):
+ if i != j and points[i].mark == points[j].mark:
+ new_distance = euclidean_distance(points[i],points[j])
+ if local_nn == 0:
+ local_nn = new_distance
+ elif new_distance < local_nn:
+ local_nn = new_distance
+
+ total += local_nn
+
+ mean_d = total/num_of_points
+
+ return mean_d
+
+
+def permutations(p = 99, n = 100):
+ list_means = []
+
+ for i in range(p):
+ marks = ["elf", "dwarf", "human", "orc"]
+ rand_points = create_random_marked_points(n, marks)
+ newMean = average_nearest_neighbor_distance(rand_points)
+ list_means.append(newMean)
+
+ return list_means
+
+
+def find_crit_points(list_means):
+ entries = list_means
+ maxEntry = 0
+ minEntry = 2
+ for i in range(len(list_means)):
+ if entries[i] > maxEntry:
+ maxEntry = entries[i]
+ if entries[i] < minEntry:
+ minEntry = entries[i]
+
+ return minEntry,maxEntry
+
+
+def crit_point_check(minEntry, maxEntry, observed):
+ if observed < minEntry or observed > maxEntry:
+ return True
+ else:
+ return False
+
+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]
+ numOfPoints = len(points)
+ for i in range(numOfPoints):
+ #Check for min and max x
+ if points[i][0] < mbr[0]:
+ mbr[0] = points[i][0]
+ if points[i][0] > mbr[2]:
+ mbr[2] = points[i][0]
+ #Check for min and max y
+ if points[i][1] < mbr[1]:
+ mbr[1] = points[i][1]
+ if points[i][1] > mbr[3]:
+ mbr[3] = points[i][1]
+
+ return mbr
\ No newline at end of file
diff --git a/view.py b/view.py
new file mode 100644
index 0000000..a1287de
--- /dev/null
+++ b/view.py
@@ -0,0 +1,38 @@
+import sys
+from PyQt4 import QtGui
+
+
+class Example(QtGui.QMainWindow):
+
+ def __init__(self):
+ super(Example, self).__init__()
+
+ self.initUI()
+
+
+ def initUI(self):
+
+ exitAction = QtGui.QAction(QtGui.QIcon('exit.png'), 'Exit', self)
+ exitAction.setShortcut('Ctrl+Q')
+ exitAction.triggered.connect(QtGui.qApp.quit)
+
+ webview = QtGui.QWebView()
+
+ self.toolbar = self.addToolBar('Exit')
+ self.toolbar.addAction(exitAction)
+
+ self.setGeometry(300, 300, 300, 200)
+ self.setWindowTitle('Assignment_09')
+ self.setWindowIcon(QtGui.QIcon('globe.png'))
+ self.show()
+
+
+def main():
+
+ app = QtGui.QApplication(sys.argv)
+ ex = Example()
+ sys.exit(app.exec_())
+
+
+if __name__ == '__main__':
+ main()
\ No newline at end of file