def rank(filename,criterion,num):
#read data and rank authors based on criteria, and output first num authors
criteria = ['Citations','NumPapers','Co-Authors','Citation Rate']
if criterion not in criteria:
print('Error: criterion can only be one of the following strings: Citations/NumPapers/Co-Authors')
return []
f1 = open(filename,'r+',encoding='utf-8')
rawdata=csv.reader(f1)
authordict = Analyze.dictauthor(rawdata)
f1.close()
ranking = []
for author in authordict.keys():
if criterion == 'Co-Authors':
heappush(ranking, [-len(authordict[author][criterion]), author])
elif criterion == 'Citation Rate':
heappush(ranking, [-(authordict[author]['Citations']/authordict[author]['NumPapers']), author])
else:
heappush(ranking, [-authordict[author][criterion], author])
bestofcriterion = []
while len(bestofcriterion)<num:
pick = heappop(ranking)
bestofcriterion.append([-pick[0],pick[1]])
return bestofcriterion
def idauthor(filename,rawlabel): #read file and find the profs closest match to rawlabel and confirm/return actual label f1 = open(filename,'r+',encoding='utf-8') rawdata=csv.reader(f1) authordict = Analyze.dictauthor(rawdata) f1.close() return Analyze.idauthor(authordict,rawlabel)
def pltCitRate(filename):
##read data from csv file, plot the clarity-citations data
f1 = open(filename,'r+',encoding='utf-8')
rawdata=csv.reader(f1)
authordict = Analyze.dictauthor(rawdata)
f1.close()
data = numpy.zeros((2,len(authordict)))
i=0
for author in authordict.keys():
data[0,i] = authordict[author]['NumPapers']
data[1,i] = authordict[author]['Citations']/authordict[author]['NumPapers']
i += 1
pyp.plot(data[0],data[1],'ro')
pyp.xlabel('Number of papers published')
pyp.ylabel('Average citations per paper')
pyp.show()
pyp.close()
return data
