authorName] = interestingauthors.get(
authorName, 0) + 1
authorscores[(
authorName, areaname,
year)] = authorscores.get(
(authorName, areaname, year), 0) + 1.0
authorscoresAdjusted[(
authorName, areaname,
year)] = authorscoresAdjusted.get(
(authorName, areaname, year),
0) + 1.0 / authorsOnPaper
return (interestingauthors, authorscores, authorscoresAdjusted, authlogs)
fdict = csv2dict_str_str('faculty-affiliations.csv')
(intauthors_gl, authscores_gl, authscoresAdjusted_gl,
authlog_gl) = parseDBLP(fdict)
f = open('generated-author-info.csv', 'w')
f.write('"name","dept","area","count","adjustedcount","year"\n')
for (authorName, area, year) in authscores_gl:
count = authscores_gl[(authorName, area, year)]
countAdjusted = authscoresAdjusted_gl[(authorName, area, year)]
f.write(authorName.encode('utf-8'))
f.write(',')
f.write((fdict[authorName]).encode('utf-8'))
f.write(',')
f.write(area)
f.write(',')
)
+ 1.0
)
authorscoresAdjusted[
(authorName, areaname, year)
] = (
authorscoresAdjusted.get(
(authorName, areaname, year), 0
)
+ 1.0 / authorsOnPaper
)
return (interestingauthors, authorscores, authorscoresAdjusted, authlogs)
fdict = csrankings.csv2dict_str_str("faculty-affiliations.csv")
(intauthors_gl, authscores_gl, authscoresAdjusted_gl, authlog_gl) = parseDBLP(
fdict
)
f = open("all-author-info.csv", "w")
f.write('"name","dept","area","count","adjustedcount","year"\n')
for (authorName, area, year) in authscores_gl:
count = authscores_gl[(authorName, area, year)]
countAdjusted = authscoresAdjusted_gl[(authorName, area, year)]
# f.write(authorName.encode('utf-8'))
f.write(authorName)
f.write(",")
# f.write((fdict[authorName]).encode('utf-8'))
f.write((fdict[authorName]))