import foursquare
import pandas as pd

#ACCESS_TOKEN = ""
#client = foursquare.Foursquare(access_token=ACCESS_TOKEN)

CLIENT_ID = ""
CLIENT_SECRET = ""
client = foursquare.Foursquare(client_id=CLIENT_ID, client_secret=CLIENT_SECRET)

# bbox = [11.109872,47.815652,12.068588,48.397136] # bounding box for Munich
# bbox = [13.088400,52.338120,13.761340,52.675499] # bounding box for Berlin
bbox = [5.866240,47.270210,15.042050,55.058140] # bounding box for Germany

new_crawl = [] # list of locations to be crawled
done = [] # list of crawled locations
links = [] # list of tuples that represent links between locations
venues = pd.DataFrame() # dictionary of locations id => meta-data on location

to_crawl = ["4ade0ccef964a520246921e3", "4cbd1bfaf50e224b160503fc", "4b0674e2f964a520f4eb22e3"]
depth = 8

for i in range(depth):
    new_crawl = []
    print "Step " + str(i) + ": " + str(len(venues)) + " locations and " + str(len(links)) + " links. " + str(len(to_crawl)) + " venues to go."
    for v in to_crawl:
        if v not in venues:
            res = client.venues(v)
            venues = venues.append(pd.DataFrame({"name":res["venue"]["name"],"users":res["venue"]["stats"]["usersCount"],
            "checkins":res["venue"]["stats"]["checkinsCount"], "lat":res["venue"]["location"]["lat"], 
            "lng":res["venue"]["location"]["lng"]}, index=[v]))
        next_venues = client.venues.nextvenues(v)
        for nv in next_venues['nextVenues']['items']:
            if ((nv["location"]["lat"] > bbox[1]) & (nv["location"]["lat"] < bbox[3]) & 
                (nv["location"]["lng"] > bbox[0]) & (nv["location"]["lng"] < bbox[2])):
                if nv["id"] not in venues:
                    venues = venues.append(pd.DataFrame({"name":nv["name"],"users":nv["stats"]["usersCount"],
                    "checkins":nv["stats"]["checkinsCount"], "lat":nv["location"]["lat"], 
                    "lng":nv["location"]["lng"]}, index=[nv["id"]]))
                if (nv["id"] not in done) & (nv["id"] not in to_crawl) & (nv["id"] not in new_crawl):
                    new_crawl.append(nv["id"])
                links.append((v, nv["id"]))
        done.append(v)
    to_crawl = new_crawl

venues = venues.reset_index().drop_duplicates(cols='index',take_last=True).set_index('index')
venues.head()

labels = venues["name"].to_dict()

import networkx as nx
G = nx.DiGraph()
G.add_nodes_from(venues.index)
for f,t in links:
    G.add_edge(f, t)

nx.info(G)

pagerank = nx.pagerank(G,alpha=0.9)
betweenness = nx.betweenness_centrality(G)

venues['pagerank'] = [pagerank[n] for n in venues.index]
venues['betweenness'] = [betweenness[n] for n in venues.index]

import matplotlib.pyplot as plt
fig = plt.figure(figsize=(8, 6), dpi=150)
ax = fig.add_subplot(111)
venues.sort('users', inplace=True)
venues.set_index('name')[-20:].users.plot(kind='barh')
ax.set_ylabel('Location')
ax.set_xlabel('Users')
ax.set_title('Top 20 Locations by Users')
plt.show()

fig = plt.figure(figsize=(8, 6), dpi=150)
ax = fig.add_subplot(111)
venues.sort('checkins', inplace=True)
venues.set_index('name')[-20:].checkins.plot(kind='barh')
ax.set_ylabel('Location')
ax.set_xlabel('Checkins')
ax.set_title('Top 20 Locations by Checkins')
plt.show()

fig = plt.figure(figsize=(8, 6), dpi=150)
ax = fig.add_subplot(111)
venues.sort('pagerank', inplace=True)
venues.set_index('name')[-20:].pagerank.plot(kind='barh')
ax.set_ylabel('Location')
ax.set_xlabel('Pagerank')
ax.set_title('Top 20 Locations by Pagerank')
plt.show()

fig = plt.figure(figsize=(8, 6), dpi=150)
ax = fig.add_subplot(111)
venues.sort('betweenness', inplace=True)
venues.set_index('name')[-20:].betweenness.plot(kind='barh')
ax.set_ylabel('Location')
ax.set_xlabel('Pagerank')
ax.set_title('Top 20 Locations by Betweenness Centrality')
plt.show()

fig = plt.figure(figsize=(16, 9), dpi=150)
graph_pos=nx.spring_layout(G)
nodesize = [10000*n for n in pagerank.values()]
nx.draw_networkx_nodes(G,graph_pos,node_size=nodesize, alpha=0.5, node_color='blue')
nx.draw_networkx_edges(G,graph_pos,width=1, alpha=0.3,edge_color='blue')
nx.draw_networkx_labels(G, graph_pos, labels=labels, font_size=10, font_family='Arial')
plt.axis('off')
plt.show()

nx.write_graphml(G, "./location_graph.graphml")