def get_stats_from_valid_locations(boundary_id, minimum_number_of_checkins_per_user, minimum_number_of_checkins_per_location, minimum_number_of_checkins_per_location_per_user):
input_file = location_objects_with_minumum_checkins_at_both_location_and_users_file%(boundary_id, minimum_number_of_checkins_per_user, minimum_number_of_checkins_per_location, minimum_number_of_checkins_per_location_per_user)
set_of_users, no_of_checkins, no_of_locations = set(), 0.0, 0.0
for data in iterateJsonFromFile(input_file):
for user, _ in data['c']: set_of_users.add(user)
no_of_checkins+=len(data['c'])
no_of_locations+=1
print 'No. of users: ', len(set_of_users)
print 'No. of checkins: ', no_of_checkins
print 'No. of valid locations: ', no_of_locations
def plot_geo_distribution_in_social_networks():
total_checkins = 0.0
for social_network in [FOURSQUARE_ID, BRIGHTKITE_ID, GOWALLA_ID]:
print social_network
ax = plt.subplot(111)
tuples_of_location_and_location_occurences_count = [(getLocationFromLid(data['key'].replace('_', ' ')), data['distribution'][social_network])
for i, data in enumerate(iterateJsonFromFile(lidsToDistributionInSocialNetworksMapFile%BOUNDARY_ID))\
if social_network in data['distribution'] and data['distribution'][social_network]>25]
tuples_of_location_and_location_occurences_count = sorted(tuples_of_location_and_location_occurences_count, key=itemgetter(1))
locations, colors = zip(*tuples_of_location_and_location_occurences_count)
sc = plotPointsOnWorldMap(locations, blueMarble=False, bkcolor='#CFCFCF', c=colors, cmap='cool', lw = 0)
divider = make_axes_locatable(ax)
# plt.title('Jaccard similarity with New York')
cax = divider.append_axes("right", size="5%", pad=0.05)
plt.colorbar(sc, cax=cax)
# for k, v in tuples_of_location_and_location_occurences_count:
# print social_network, k, v
# print len(tuples_of_location_and_location_occurences_count)
plt.show()
def load_checkins_graph(checkins_graph_file):
graph = nx.Graph()
for data in iterateJsonFromFile(checkins_graph_file):
(u, v) = data['e'].split('__')
graph.add_edge(u , v, {'w': data['w']})
noOfClusters, clusters = clusterUsingAffinityPropagation(graph)
# for cluster in clusters:
# print len(cluster), cluster
nodeToClusterIdMap = dict(clusters)
colorMap = dict([(i, GeneralMethods.getRandomColor()) for i in range(noOfClusters)])
clusters = [(c, list(l)) for c, l in groupby(sorted(clusters, key=itemgetter(1)), key=itemgetter(1))]
points, colors = zip(*map(lambda l: (getLocationFromLid(l.replace('_', ' ')), colorMap[nodeToClusterIdMap[l]]), graph.nodes()))
_, m =plotPointsOnWorldMap(points[:1], s=0, lw=0, c=colors[:1], returnBaseMapObject=True)
for u, v, data in graph.edges(data=True):
if nodeToClusterIdMap[u]==nodeToClusterIdMap[v]:
color, u, v, w = colorMap[nodeToClusterIdMap[u]], getLocationFromLid(u.replace('_', ' ')), getLocationFromLid(v.replace('_', ' ')), data['w']
m.drawgreatcircle(u[1],u[0],v[1],v[0],color=color, alpha=1.5)
# plt.title(title)
plt.show()
print noOfClusters
print graph.number_of_edges()
print graph.number_of_nodes()