def temporalLocalityTemporalDistanceExample(lattice=NEW_YORK):
distances = defaultdict(dict)
for latticeObject in FileIO.iterateJsonFromFile(hashtagsLatticeGraphFile%('training_world','%s_%s'%(2,11))):
if latticeObject['id']==lattice:
latticeHashtagsSet = set(latticeObject['hashtags'])
for neighborLattice, neighborHashtags in latticeObject['links'].iteritems():
distances[neighborLattice] = {}
neighborHashtags = filterOutNeighborHashtagsOutside1_5IQROfTemporalDistance(latticeObject['hashtags'], neighborHashtags, findLag=False)
neighborHashtagsSet = set(neighborHashtags)
distances[neighborLattice]['similarity']=len(latticeHashtagsSet.intersection(neighborHashtagsSet))/float(len(latticeHashtagsSet.union(neighborHashtagsSet)))
distances[neighborLattice]['temporalDistance']=np.mean([abs(latticeObject['hashtags'][k][0]-neighborHashtags[k][0]) for k in neighborHashtags if k in latticeObject['hashtags']])/(60.*60.)
distances[neighborLattice]['geoDistance']=getHaversineDistanceForLids(latticeObject['id'].replace('_', ' '), neighborLattice.replace('_', ' '))
break
dataPoints = []
ax = plt.subplot(111)
for k, data in distances.iteritems(): dataPoints.append((getLocationFromLid(k.replace('_', ' ')), data['temporalDistance']))
points, colors = zip(*sorted(dataPoints, key=itemgetter(1)))
sc = plotPointsOnWorldMap(points, blueMarble=False, bkcolor='#CFCFCF', cmap='RdPu', c=colors, lw = 0, alpha=1.0)
plotPointsOnWorldMap([getLocationFromLid(lattice.replace('_', ' '))], blueMarble=False, bkcolor='#CFCFCF', c='#64FF1C', lw = 0)
divider = make_axes_locatable(ax)
plt.title('Average time difference from New York')
cax = divider.append_axes("right", size="5%", pad=0.05)
plt.colorbar(sc, cax=cax)
# plt.show()
plt.savefig('../images/temporalDistanceExample.png')
def _getDistances():
distances = {}
for latticeObject in FileIO.iterateJsonFromFile(hashtagsLatticeGraphFile%('training_world','%s_%s'%(2,11))):
latticeHashtagsSet = set(latticeObject['hashtags'])
for neighborLattice, neighborHashtags in latticeObject['links'].iteritems():
key = '_'.join(sorted([latticeObject['id'], neighborLattice]))
if key not in distances:
distances[key] = {}
neighborHashtags = filterOutNeighborHashtagsOutside1_5IQROfTemporalDistance(latticeObject['hashtags'], neighborHashtags, findLag=False)
neighborHashtagsSet = set(neighborHashtags)
distances[key]['similarity']=len(latticeHashtagsSet.intersection(neighborHashtagsSet))/float(len(latticeHashtagsSet.union(neighborHashtagsSet)))
distances[key]['temporalDistance']=np.mean([abs(latticeObject['hashtags'][k][0]-neighborHashtags[k][0]) for k in neighborHashtags if k in latticeObject['hashtags']])
distances[key]['geoDistance']=getHaversineDistanceForLids(latticeObject['id'].replace('_', ' '), neighborLattice.replace('_', ' '))
return distances
