for acn, datedict in acnt_date.items():
painter.setAcntName(acn)
'''
xlabel means std
'''
xlabel = []
means = []
stds = []
sorted_tuple = sorted(datedict.items(), key=operator.itemgetter(0))
for tup in sorted_tuple:
statis_helper.setArray(tup[1])
means.append(statis_helper.getavg())
stds.append(statis_helper.getstd())
xlabel.append(tup[0].split('_')[1])
painter.plotBar(xlabel,means,stds,acn+"_AvgTweetPerUrlAtDifferentTime")
painter.plotBar(xlabel,[len(tup[1]) for tup in sorted_tuple],None,acn+"_AvgUrlAtDifferentTime")
'''
acnt_url[acnt][k].append(v)
'''
totalsum = []
acnt_all_url = acnt_url[acn]
for v in acnt_all_url.values():
totalsum = elementAdd(totalsum,v)
totalsum = elementDiv(totalsum, len(acnt_all_url))
# for
painter.plotBar([i+1 for i in xrange(len(totalsum))],totalsum,None,acn+"_Url_Num")
do classification using kmeans
then plot the centra
'''
# from sklearn.cluster import KMeans
# km = KMeans(n_clusters=3)
# km.fit(avg_filt)
# centras = km.cluster_centers_
xlabel = [int(x) for x in times_label]
# class_percentage = {}
# for i in xrange(km.n_clusters):
# class_percentage[i] = 0
# for i in km.labels_:
# class_percentage[i]+=1
# for i in xrange(km.n_clusters):
# print class_percentage[i]*1.0/len(km.labels_)
# for idx, data in enumerate(centras):
# painter.plotLine(xlabel,data,str(idx))
for data in avg_filt:
painter.plotLine(xlabel,data,None,'r*')
painter.showplot(xlabel)
#painter.showLegend()
painter.newFigure()
painter.plotLine(xlabel, time_counts,"number of new urls","r")
def cumulativeSum(vlist,start = 0):
for v in vlist:
start+= v
yield start
painter.plotBar(xlabel,[len(t) for t in time_urls.values()],None,"Hourly URLs","Communicative URLs")
painter.showplot(xlabel)
print "finish!"
