def estimateUpperRangeForTimePeriod(timeRange, outputFolder):
dataX = []
i=1
for hashtagObject in FileIO.iterateJsonFromFile(hashtagsWithoutEndingWindowFile%(outputFolder,'%s_%s'%timeRange)):
print i;i+=1
occuranesInHighestActiveRegion = getOccuranesInHighestActiveRegion(hashtagObject)
dataX.append((occuranesInHighestActiveRegion[-1][1]-occuranesInHighestActiveRegion[0][1])/TIME_UNIT_IN_SECONDS)
print getOutliersRangeUsingIRQ(dataX)
plt.hist(dataX, bins=10)
plt.show()
def plotHashtagSourcesOnMap(timeRange, outputFolder):
i = 1
distribution = defaultdict(int)
for hashtagObject in FileIO.iterateJsonFromFile(hashtagsFile%(outputFolder,'%s_%s'%timeRange)):
occuranesInHighestActiveRegion, isFirstActiveRegion = getOccuranesInHighestActiveRegion(hashtagObject, True)
if occuranesInHighestActiveRegion:
source, count = getSourceLattice(occuranesInHighestActiveRegion)
print i, source;i+=1
distribution[getLidFromLocation(source)]+=1
# if i==10: break
points, colors = zip(*[(getLocationFromLid(k),v) for k, v in sorted(distribution.iteritems(), key=itemgetter(1))])
cm = matplotlib.cm.get_cmap('Paired')
sc = plotPointsOnWorldMap(points, c=colors, cmap=cm, lw = 0)
plt.colorbar(sc)
plt.show()
