def mendeleyDisciplines():
def generalCondition(doc):
time = doc.publicationDatetime()
return (
(time.year == 2012 and time.month >= 6 and time.month <= 8) or
(time.year > 2012)
) and doc.mendeleyDisciplines != None
def domainDocs(domain):
return [doc for doc in consideredDocs if generalCondition(doc) and domain in doc.mendeleyDisciplines]
consideredDocs = list(filter(lambda doc: generalCondition(doc), SimpleDoc.getall()))
totalDocs = len(consideredDocs)
distinctDomains = set()
for doc in consideredDocs:
distinctDomains |= set(doc.mendeleyDisciplines)
domainData = []
for domain in distinctDomains:
d = domainDocs(domain)
numDocs = len(d)
meanTweets = numpy.mean([doc.numTweets() for doc in d])
domainData.append((domain, numDocs, ("%2.2f" % (float(numDocs)*100 / totalDocs)) + "\\%", "%2.2f" % meanTweets ))
domainDataSorted = sorted(domainData, key=lambda x: x[1], reverse=True)
compileTex(
simpleTabular(["Disziplin", "\\#Dokumente", "Anteil", "AvgTweets"], domainDataSorted, orientation="lrrr"),
figurePath("mendeleyDisciplines2.pdf")
)
def groupByJournalAndVolume():
issns = { }
docs = list(SimpleDoc.getall())
for doc in docs:
issns[doc.issn] = issns.get(doc.issn, 0) + 1
validIssns = map(lambda kv: kv[0], filter(lambda item: item[1]>5 and item[0] != None, issns.items()))
groups = { }
for doc in docs:
if doc.issn in validIssns:
groupList = groups.get((doc.issn, doc.volume), [])
# groupList = groups.get(doc.issn, [])
groupList.append(doc)
groups[(doc.issn, doc.volume)] = groupList
# groups[doc.issn] = groupList
validGroups = filter(lambda group: len(group[1]) > 5, groups.items())
# validGroups = groups.items()
correlationValues = []
for ident, docs in validGroups:
docTweets = map(lambda doc: doc.numTweets(), docs)
docCrossrefs = map(lambda doc: doc.numCrossrefs(), docs)
korr = None
# docTweetCrossrefRatios = map(lambda doc: [float(doc.numTweets()) / doc.numCrossrefs() if doc.numCrossrefs() != 0 else float('nan')], docs)
maxYear = max(map(lambda doc: doc.publicationDatetime().year, docs))
minYear = min(map(lambda doc: doc.publicationDatetime().year, docs))
yearRange = None
if maxYear == minYear:
yearRange = str(minYear)
else:
yearRange = str(minYear) + "-" + str(maxYear)
try:
korr = "%2.3f" % korrelationskoeffizient(docTweets, docCrossrefs)
except ZeroDivisionError:
korr = "NaN"
# correlationValues.append([ident[0], ident[1], len(docs), "%2.2f" % numpy.mean(docTweets), "%2.2f" % numpy.std(docTweets), korr, yearRange])
correlationValues.append([ident[0], ident[1], len(docs), "%2.2f" % numpy.mean(docTweets), "%2.2f" % numpy.mean(docCrossrefs), "%2.2f" % (float(numpy.sum(docTweets))/numpy.sum(docCrossrefs)), yearRange])
# correlationValues.append([ident, len(docs), "%2.2f" % numpy.mean(docTweets), "%2.2f" % numpy.std(docTweets), korr])
correlationValues = sorted(correlationValues, key=lambda x: x[0])
compileTex(
# simpleTabular(["ISSN", "Volume", "\\#Docs", "AVG Tweets", "StdDev", "korr", "Years"], correlationValues, orientation="llrrrrl"),
simpleTabular(["ISSN", "Volume", "\\#Docs", "AVG T", "AVG C", "T/C", "Years"], correlationValues, orientation="llrrrrl"),
# simpleTabular(["ISSN", "\\#Docs", "AVG Tweets", "StdDev", "korr", "Years"], correlationValues, orientation="lrrrrl"),
figurePath("correlationsInJournals2.pdf")
)
