print len(url_nofilt) statis_helper = util.statis(None) statis_helper.setArray([sum(x) for x in url_filt.values()]) print statis_helper.getreport() statis_helper.setArray([sum(x) for x in url_nofilt.values()]) print(statis_helper.getreport()) ''' one account avg single tweet amount daily trend ''' from PlotView import PlotView import operator from util import statis painter = PlotView() statis_helper = statis(None) def elementAdd(l1, l2): if len(l1) > len(l2): return elementAdd(l2, l1) return map(add,l1, l2[:len(l1)])+l2[len(l1):] def elementDiv(l, d): if d == 0: return l return [it*1.0/d for it in l] for acn, datedict in acnt_date.items(): painter.setAcntName(acn) ''' xlabel means std '''
statis_helper = util.statis(None) avg_filt = [[t[0]/t[1] for t in xlist] for xlist in url_filt.values()] sum_filt = [reduce(lambda x,y: x+y[0], xlist,0 ) for xlist in url_filt.values()] statis_helper.setArray(sum_filt) print statis_helper.getreport() avg_nofilt = [[t[0]/t[1] for t in xlist] for xlist in url_nofilt.values()] sum_nofilt = [reduce(lambda x,y: x+y[0], xlist,0 ) for xlist in url_nofilt.values()] statis_helper.setArray(sum_nofilt) print(statis_helper.getreport()) from PlotView import PlotView import operator from util import statis painter = PlotView() statis_helper = statis(None) def elementAdd(l1, l2): if len(l1) > len(l2): return elementAdd(l2, l1) return map(add,l1, l2[:len(l1)])+l2[len(l1):] def elementDiv(l, d): if d == 0: return l return [it*1.0/d for it in l] ''' first we want to find some pattern url_filt do classification using kmeans then plot the centra ''' # from sklearn.cluster import KMeans