def removeRetweetsAbsolute():
l = [ ]
for doc in SimpleDoc.getall():
if len(doc.tweets) >= 1:
numNormalTweets = sum(1 for tweet in doc.tweets if not tweet.isRetweet())
numRetweets = sum(1 for tweet in doc.tweets if tweet.isRetweet())
l.append([numNormalTweets, numRetweets])
diffs = map(lambda x: x[0]-x[1], l)
"""relBins, labels = pieData([
[lambda x: x==0, "diff: 0"],
[lambda x: x==1, "diff: 1"],
[lambda x: x>=2 and x <= 5, "diff: 2-5"],
[lambda x: x>5, "diff: >5"]
], diffs)"""
relBins, labels = pieData([
[lambda x: x>=6 and x <=10, "diff: 6-10"],
[lambda x: x>=11 and x <=50, "diff: 11-50"],
[lambda x: x>51, "diff: >51"]
], diffs)
plt.figure()
plt.pie(relBins, autopct='%1.1f%%',
startangle=90, labels=labels)
plt.title("Differenzen in Anzahl Tweets zu Dokument, wenn Retweets entfernt werden.")
plt.show()
def removeRetweetsRelative():
l = [ ]
for doc in SimpleDoc.getall():
if len(doc.tweets) >= 1:
numNormalTweets = sum(1 for tweet in doc.tweets if not tweet.isRetweet())
numRetweets = sum(1 for tweet in doc.tweets if tweet.isRetweet())
l.append([numNormalTweets+numRetweets, numNormalTweets])
diffs = map(lambda x: float(x[0]-x[1])/x[0], l)
relBins, labels = pieData([
[lambda x: x==0.0, "0%"],
[lambda x: x>0.0 and x<=0.3, "0%<d<=30%"],
[lambda x: x>0.3 and x<=0.5, "30%<d<=50%"],
[lambda x: x>0.5, ">50%"]
], diffs)
plt.figure()
plt.pie(relBins, autopct='%1.1f%%',
startangle=90, labels=labels)
plt.title("Differenzen in Prozent, Retweets entfernt werden")
plt.show()
def publication_years():
plt.figure(num=None, figsize=(8, 4), dpi=80, facecolor='w', edgecolor='k')
publicationYears = list(simpleDoc.publicationDatetime().year for simpleDoc in SimpleDoc.getallBetween(None, None))
histDisc(plt, publicationYears, width = 0.5)
# plt.savefig(figurePath("publication_years.png"))
plt.tight_layout()
plt.show()
def tweetVsMendeleyReaders(yearBounds = [None, None], maxTweets = 300, maxReaders = 300):
tweetVsMendeleyReaderList = []
totalDocs = 0
for doc in filter(lambda doc:
(doc.mendeleyReaders != None and doc.mendeleyReaders<=maxReaders) and
(doc.tweets != None and len(doc.tweets)<=maxTweets) and
(not yearBounds[0] or doc.publicationDatetime().year>=yearBounds[0]) and
(not yearBounds[1] or doc.publicationDatetime().year<=yearBounds[1]),
SimpleDoc.getall()
):
tweetVsMendeleyReaderList.append([len(doc.tweets), doc.mendeleyReaders])
totalDocs += 1
x, y = zip(*tweetVsMendeleyReaderList)
plt.figure()
plt.scatter(x, y)
plt.title("Korrelation zwischen Tweets und Zitationen (Papieren zwischen " + str(yearBounds[0]) + " und " + str(yearBounds[1]) + "; #Docs: " + str(totalDocs) + ")")
plt.ylabel("#Tweets (1-" + str(maxTweets) + ")")
plt.xlabel("#Readers (1-" + str(maxReaders) + ")")
p = numpy.polyfit(x, y, 1)
xTrend = range(min(x), max(x)+1)
yTrend = map(lambda x: numpy.polyval(p, x), xTrend)
plt.plot(xTrend, yTrend, color='r')
plt.figtext(0.80, 0.05, 'korrelationskoeffizient: ' + str(korrelationskoeffizient(x, y)))
plt.show()
def numTweets():
plt.figure()
numTweets = list(len(simpleDoc.tweets) for simpleDoc in SimpleDoc.getall())
labels, values = hist(numTweets, [1, 2, 5, 10, 20, 50, 100, 500, 1000])
barPlot(plt, labels, values)
plt.show()
def removeDoubleUsersAbsolute():
l = [ ]
for doc in SimpleDoc.getall():
tweetUsers = map(lambda x: x.user, doc.tweets)
if len(tweetUsers) >= 1:
l.append([len(tweetUsers), len(set(tweetUsers))])
diffs = map(lambda x: x[0]-x[1], l)
"""relBins, labels = pieData([
[lambda x: x==0, "diff: 0"],
[lambda x: x==1, "diff: 1"],
[lambda x: x>=2 and x <= 5, "diff: 2-5"],
[lambda x: x>5, "diff: >5"]
], diffs)"""
relBins, labels = pieData([
[lambda x: x>=6 and x<=10, "diff: 5-10"],
[lambda x: x>=11 and x<=50, "diff: 11-50"],
[lambda x: x>=51 and x<=300, "diff: 51-300"]
], diffs)
plt.figure()
plt.pie(relBins, autopct='%1.1f%%',
startangle=90, labels=labels)
plt.title("Differenzen in Anzahl Tweets zu Dokument, wenn doppelte Benutzer entfernt werden.")
plt.show()
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 twitterHist():
plt.figure(num=None, figsize=(8, 4), dpi=80, facecolor='w', edgecolor='k')
tweetTime = [(tweet.datetime().year, tweet.datetime().month) for doc in SimpleDoc.getall() for tweet in doc.tweets]
histDisc(plt, tweetTime, width=0.5)
#plt.title("Verteilung der Tweets nach Jahr und Monat")
plt.tight_layout()
plt.show()
def crossrefVsTwitter(yearBounds = [None, None], minTweetAge = None, maxTweetAge = None):
tweetVsCrossrefList = []
minTweetAge = 60*60*24*0
maxTweetAge = 60*60*24*100
totalDocs = 0
totalTweets = 0
nullWeights = 0
nonNullWeights = 0
for doc in filter(
lambda doc:
(doc.publicationDatetime().year==2012 and
doc.publicationDatetime().month>=6 and doc.publicationDatetime().month<=8),
SimpleDoc.getall()
):
docsTweets = filter(lambda tweet:
(not minTweetAge or (tweet.timestamp-doc.publicationTimestamp) >= minTweetAge) and
(not maxTweetAge or (tweet.timestamp-doc.publicationTimestamp) <= maxTweetAge),
doc.tweets)
def userWeight(tweet):
user = tweet.user()
return None if user is None else user.weight()
userWeights = map(lambda tweet: userWeight(tweet), docsTweets)
nullWeights += sum((1 for weight in userWeights if weight is None))
nonNullWeights += sum((1 for weight in userWeights if not weight is None))
tweetVsCrossrefList.append([doc.numCrossrefs(), 0 if len(userWeights) == 0 else sum(filter(lambda weight: weight != None, userWeights))])
totalDocs += 1
totalTweets += len(docsTweets)
print totalDocs
print totalTweets
print float(nullWeights) / (nullWeights+nonNullWeights)
# tweetVsCrossrefList = sorted(tweetVsCrossrefList, key=lambda tc: tc[1], reverse=True)[:100]
x, y = zip(*tweetVsCrossrefList)
paperFigure(plt)
plt.scatter(x, y)
# plt.title("Korrelation zwischen Tweets und Zitationen (Papieren zwischen " + str(yearBounds[0]) + " und " + str(yearBounds[1]) + "; #Docs: " + str(totalDocs) + ")")
plt.ylabel("#Tweets")
plt.xlabel("#Crossrefs")
#plt.xlim((0,200))
#plt.ylim((0,30))
p = numpy.polyfit(x, y, 1)
xTrend = range(min(x), max(x)+1)
yTrend = map(lambda x: numpy.polyval(p, x), xTrend)
plt.plot(xTrend, yTrend, color='r')
# plt.figtext(0.80, 0.05, 'korrelationskoeffizient: ' + str(korrelationskoeffizient(x, y)))
print 'korrelationskoeffizient: ' + str(korrelationskoeffizient(x, y))
plt.tight_layout()
plt.show()
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")
)
def overviewData():
# earliest tweet timestamp: 1337079632
docs = list(SimpleDoc.getall())
tweets = [ tweet for doc in docs for tweet in doc.tweets ]
print "total Tweets: " + str(len(tweets))
print "latest tweet: " + str(max(tweets, key=lambda x: x.timestamp).datetime())
print "earliest tweet: " + str(min(tweets, key=lambda x: x.timestamp).datetime())
print "total Documents: " + str(len(docs))
print "latest document: " + str(max(docs, key=lambda doc: doc.publicationTimestamp).publicationDatetime())
print "earliest document: " + str(min(docs, key=lambda doc: doc.publicationTimestamp).publicationDatetime())
def tweetsBySpecificUserCorrelations():
docs = SimpleDoc.getallBetween((2012, 6), (2012, 8))
pairs = []
for doc in docs:
numTweets = len(filter(lambda tweet: tweet.username=="ATP_CME" ,doc.tweets))
citations = doc.averageCitations()
pairs.append([numTweets, citations])
x, y = zip(*pairs)
print allCorrelations(x, y)
plt.scatter(x, y)
plt.show()
def userHist():
plt.figure()
users = []
for doc in SimpleDoc.getall():
for tweet in doc.tweets:
users.append(tweet.user)
userGroupCounts = sorted(groupCount(users), key=lambda x: x[1], reverse=True)
filteredUserGroupCounts = filter(lambda x: x[1]>=2, userGroupCounts)
plt.plot(map(lambda x: x[1], filteredUserGroupCounts), range(1, len(filteredUserGroupCounts)+1))
plt.title("Users sortiert nach Tweets (1er User abgeschnitten)")
plt.xlabel("Rang des Users")
plt.ylabel("#Tweets")
plt.show()
def tweetHist():
docs = list(filter(
lambda doc:
(doc.publicationDatetime().year==2012 and
doc.publicationDatetime().month>=6 and doc.publicationDatetime().month<=8),
SimpleDoc.getall()
))
numTweets = [doc.numTweets() for doc in docs]
plt.figure()
plt.hist(numTweets, bins=xrange(0, 150, 5))
plt.figure()
numCite = [doc.numCrossrefs() for doc in docs]
plt.hist(numCite, bins=xrange(0, 150, 5))
plt.show()
def topUsers():
plt.figure()
users = []
for doc in SimpleDoc.getall():
for tweet in doc.tweets:
users.append(tweet.user)
userGroupCounts = sorted(groupCount(users), key=lambda x: x[1], reverse=True)
topUsers = userGroupCounts[:10]
users, values = zip(*topUsers)
barPlot(plt, list(users), list(values))
plt.title("Top 10 Users")
plt.ylabel("#Tweets")
plt.show()
def numRetweets():
numNormalTweets = 0
numRetweets = 0
for doc in SimpleDoc.getall():
for tweet in doc.tweets:
if(tweet.isRetweet()):
numRetweets += 1
else:
numNormalTweets += 1
relNormalTweets, relRetweets = float(numNormalTweets)*100 / (numNormalTweets+numRetweets), float(numRetweets)*100 / (numNormalTweets+numRetweets)
plt.figure()
plt.pie([relNormalTweets, relRetweets], autopct='%1.1f%%',
startangle=90, labels=[
'normale Tweets (' + str(numNormalTweets) + ')',
'Retweets (' + str(numRetweets) + ')'
], colors=['green', 'yellow'])
plt.show()
def removeDoubleUsersRelative():
l = [ ]
for doc in SimpleDoc.getall():
tweetUsers = map(lambda x: x.user, doc.tweets)
if len(tweetUsers) >= 1:
l.append([len(tweetUsers), len(set(tweetUsers))])
diffs = map(lambda x: float(x[0]-x[1])/x[0], l)
relBins, labels = pieData([
[lambda x: x==0.0, "0%"],
[lambda x: x>0.0 and x<=10.0, "0%<d<=10%"],
[lambda x: x>0.1 and x<=0.3, "10%<d<=30%"],
[lambda x: x>0.3, ">30%"]
], diffs)
plt.figure()
plt.pie(relBins, autopct='%1.1f%%',
startangle=90, labels=labels)
plt.title("Differenzen in Anzahl Tweets zu Dokument, wenn doppelte Benutzer entfernt werden.")
plt.show()
def alteringTweetStreamAfterFirstPeak():
relativeTweetDiffAfter1WeekAndTotal = map(
lambda doc:
float(doc.numTweets()) / doc.numTweetsBetweenRelative(None, 60*60*24*7),
filter(lambda doc: doc.numTweetsBetweenRelative(None, 60*60*24*7) >= 5, SimpleDoc.getall())
)
relBins, labels = pieData([
[lambda x: x==1.0, "+0%"],
[lambda x: x>1.0 and x<=1.1, "+0-10%"],
[lambda x: x>1.1 and x<=1.2, "+10-20%"],
[lambda x: x>1.2 and x<=1.3, "+20-30%"],
[lambda x: x>1.3 and x<=1.4, "+30-40%"],
[lambda x: x>1.4 and x<=1.5, "+40-50%"],
[lambda x: x>1.5, ">50%"]
], relativeTweetDiffAfter1WeekAndTotal)
plt.figure()
plt.pie(relBins, autopct='%1.1f%%',
startangle=90, labels=labels)
plt.title("Relative Anzahl Tweets im Vergleich zu Anzahl Tweets nach einer Woche")
plt.show()
def correlationsForQuartals():
quartals = [
[3, 2008],
[0, 2009], [1, 2009], [2, 2009], [3, 2009],
[0, 2010], [1, 2010], [2, 2010], [3, 2010],
[0, 2011], [1, 2011], [2, 2011], [3, 2011],
[0, 2012], [1, 2012], [2, 2012], [3, 2012],
[0, 2013], [1, 2013]
]
def docInQuartal(doc, quartal):
if quartal[1] != doc.publicationDatetime().year:
return False
elif quartal[0] == 0:
return doc.publicationDatetime().month >=1 and doc.publicationDatetime().month<=3
elif quartal[0] == 1:
return doc.publicationDatetime().month >=4 and doc.publicationDatetime().month<=6
elif quartal[0] == 2:
return doc.publicationDatetime().month >=7 and doc.publicationDatetime().month<=9
elif quartal[0] == 3:
return doc.publicationDatetime().month >=10 and doc.publicationDatetime().month<=12
else:
raise ValueError("Argument quartal consists of a tuple [quartal, year] where quartal must be between 0 and 3")
allDocs = list(SimpleDoc.getall())
coefficients = []
for quartal in quartals:
docs = filter(lambda doc: docInQuartal(doc, quartal) and doc.mendeleyReaders != None, allDocs)
print len(docs)
x, y = zip(*map(lambda doc: [len(doc.tweets), doc.crossrefTimeline[0].totalCrossrefs], docs))
coefficients.append(korrelationskoeffizient(x, y))
plt.figure()
plt.plot(range(0, len(quartals)), coefficients)
plt.show()
# expertCategories = ['Medicine', 'Health' ]
wordExperts = getWordExperts(expertWords)
# patrickExperts = getPatrickExperts(expertCategories)
"""bioDocs = minimizedDocs(
filter(
lambda doc:
doc.mendeleyDisciplines != None and 'Biological Sciences' in doc.mendeleyDisciplines,
SimpleDoc.getallBetween((2012,6), (2012,8))
),
metrics
)"""
docs = minimizedDocs(
SimpleDoc.getallBetween((2012,6), (2012,8)),
metrics
)
usersInTimewindow = set((usr for doc in docs for usr in doc[0]))
totalNumTweets = sum((1 for doc in docs for u in doc[0]))
"""f = open("baselines", "w")
for numTweets in range(100, totalNumTweets, 100):
print str(numTweets) + " / " + str(totalNumTweets)
baseline = getBaseline(docs, metricNames, numTweets)
f.write(json.dumps( { "num-tweets" : numTweets, "baseline" : baseline } ) + "\n")
f.flush()
f.close()"""
def canBeEncoded(text):
try:
str(text)
return True
except UnicodeEncodeError:
return False
def tweetsBetweenDay(documents, lowerBound, upperBound):
return [[tweet.text, tweet.timestamp, tweet.username, doc.doi, doc.title, doc.publicationTimestamp] for doc in documents for tweet in doc.tweets
if
((lowerBound*60*60*24) <= (tweet.timestamp - doc.publicationTimestamp) <= (upperBound*60*60*24)) and
canBeEncoded(tweet.text) and
canBeEncoded(doc.title)
]
relevantDocuments = SimpleDoc.getallBetween((2012, 6), (2012, 8))
tweets = []
tweets.extend(random.sample(tweetsBetweenDay(relevantDocuments, 0, 1), 111))
tweets.extend(random.sample(tweetsBetweenDay(relevantDocuments, 1, 3), 111))
tweets.extend(random.sample(tweetsBetweenDay(relevantDocuments, 3, 5), 111))
tweets.extend(random.sample(tweetsBetweenDay(relevantDocuments, 7, 30), 333))
tweets.extend(random.sample(tweetsBetweenDay(relevantDocuments, 100, 300), 333))
tweetTexts = map(lambda tweetdata: "\t".join([str(tweetdata[0]), str(tweetdata[1]), tweetdata[2], tweetdata[3], tweetdata[4], str(tweetdata[5])]), tweets)
random.shuffle(tweetTexts)
f = open("tweetTexts_1.txt", "w")
for text in tweetTexts[0:333]:
f.write(text.replace("\n", " ").replace("\"", "").replace("'", "") + "\n")
f.close()
import json
from main.util.db import openDb
from scipy import stats
from main.util.common import SimpleDoc, powerset, Log
import math
import itertools
expertTopics = list(map(lambda s: s.strip(), open("data/expert_topics", "r")))
l = Log(filename="foo", verbose=True)
docs = map(lambda doc: [map(lambda tweet: tweet.username, doc.tweets), map(lambda metric: metric[1](doc), metrics)], SimpleDoc.getallBetween((2012,6), (2012,8)))
baseline = { }
for ind, metricName in zip(range(0, len(metricNames)), metricNames):
pairs = []
for doc in docs:
numTweets = len(doc[0])
metricScore = doc[1][ind]
pairs.append([numTweets, metricScore])
x, y = zip(*pairs)
s, p = stats.spearmanr(x, y)
baseline[metricName] = s
count = 0
count2 = 0
for ind, metricName in zip(range(0, len(metricNames)), metricNames):
pairs = []
def userCorrelationToDiscipline():
"""
zuerst user_disc_map erstellen:
[ user1 : [
[mendDisc1_1, mendDisc1_2, ...], // Liste von Disziplinen pro Tweet des Nutzers
[mendDisc2_1, mendDisc2_2, ...]
], user2: [
...
] ]
"""
if not os.path.isfile(dataPath("user_disc_map.json")):
userDiscList = []
for doc in SimpleDoc.getall():
twitterUsers = [tweet.user for tweet in doc.tweets]
disciplines = doc.mendeleyDisciplines
if len(twitterUsers)!=0 and disciplines!=None and len(disciplines)!=0:
for twitterUser in twitterUsers:
userDiscList.append([twitterUser, disciplines])
userDiscMap = {}
for item in userDiscList:
discList = userDiscMap.get(item[0], [])
discList.append(item[1])
userDiscMap[item[0]] = discList
writeJsonToData(userDiscMap, "user_disc_map.json")
else:
userDiscMap = readJsonFromData("user_disc_map.json")
"""
dann "user_disc_count_map" erstellen:
[ user1 : {
"total_posts" : n,
"user_posts_in_desc" : {
"disc1" : n_1,
"disc2" : n_2,
...
}
}, user2: {
...
} ]
"""
if not os.path.isfile(dataPath("user_disc_count_map.json")):
userDiscCountMap = { }
for user, descListList in userDiscMap.items():
totalPosts = len(descListList)
allUsersDesc = set()
for descList in descListList:
allUsersDesc |= set(descList)
userPostsInDesc = { }
for desc in allUsersDesc:
postsInDesc = sum(1 for descList in descListList if desc in descList)
userPostsInDesc[desc] = postsInDesc
userDiscCountMap[user] = { "total_posts" : totalPosts, "user_posts_in_desc" : userPostsInDesc }
writeJsonToData(userDiscCountMap, "user_disc_count_map.json")
else:
userDiscCountMap = readJsonFromData("user_disc_count_map.json")
for user, userdata in userDiscCountMap.items():
totalPosts = userdata['total_posts']
relCounts = []
for desc, count in userdata['user_posts_in_desc'].items():
relCounts.append([desc, float(count)/totalPosts])
relCounts = sorted(relCounts, key=lambda x: x[1], reverse=True)
if totalPosts > 50:
print user
print relCounts
print "\n\n"
def cummulativeTwitterPlots():
# twitterTimelines, publicationTimestamps = zip(*filter(lambda timelinePubTs: len(timelinePubTs[0]) != 0, map(lambda doc: [doc.cummulativeTwitterTimeline(), doc.publicationTimestamp], SimpleDoc.getall())))
twitterTimelines = filter(lambda tl: len(tl) != 0, map(lambda doc: map(lambda point: [point[0]-doc.publicationTimestamp, point[1]], doc.cummulativeTwitterTimeline()), SimpleDoc.getall()))
# twitterTimelines = filter(lambda tl: len(tl) < 20, twitterTimelines)
# twitterTimelines = filter(lambda tl: len(tl) > 50, twitterTimelines)
plt.figure()
for timeline in twitterTimelines:
x, y = zip(*timeline)
plt.plot(x, y)
plt.show()
def correlationTimeTweets():
x, y = zip(*map(lambda doc: [doc.publicationTimestamp, len(doc.tweets)], SimpleDoc.getall()))
print korrelationskoeffizient(x, y) # 0.082
def distFirstTweetToDoc():
allDocs = list(SimpleDoc.getall())
for param in range(20, 100):
diffs = []
maximumTweetAge = 60*60*24*param
minimumTweetAge = 60*60*24*10
for doc in filter(lambda doc: len(doc.tweets) != 0 and doc.age() >= maximumTweetAge, allDocs):
pubTimestamp = doc.publicationTimestamp
# firstTweetTimestamp = max([tweet.timestamp for tweet in doc.tweets])
diffs.extend([tweet.timestamp-pubTimestamp for tweet in
filter(
lambda tweet: (tweet.timestamp-doc.publicationTimestamp) < maximumTweetAge and (tweet.timestamp-doc.publicationTimestamp) > minimumTweetAge,
doc.tweets
)
])
maxBins = 30
timeslot = (float(maximumTweetAge)-float(minimumTweetAge))/maxBins
def binNr2Bound(binNr):
return minimumTweetAge+(binNr*timeslot)
binConditions = map(
lambda binNr: [lambda x: x>binNr2Bound(binNr) and x<=binNr2Bound(binNr+1), str(binNr) + "X"],
range(0, maxBins)
)
# binConditions.append([lambda x: x>binNr2Bound(maxBins), ">" + str(maxBins-1) + str("X")])
diffBins, diffLabels = pieData(binConditions, diffs)
distBinConditions = map(
lambda binNr: [lambda x: x==binNr, "X=" + str(binNr)],
range(0, maxBins)
)
def getBins(beta, binConditions):
s = map(lambda x: int(x), numpy.random.exponential(beta, 10000))
bins, labels = pieData(binConditions, s)
return bins
def binDiffs(bins1, bins2):
return sum(map((lambda (a, b): abs(a-b)), zip(bins1, bins2)))
def searchInRangeRec(minBeta, maxBeta, steps, depth, maxDepth):
minError = min(
map(lambda beta: [beta, binDiffs(getBins(beta, distBinConditions), diffBins)], numpy.arange(minBeta, maxBeta, steps)),
key = lambda x: x[1]
)
errorBelow = binDiffs(getBins(minError[0]-(float(steps)/2), distBinConditions), diffBins)
errorAbove = binDiffs(getBins(minError[0]+(float(steps)/2), distBinConditions), diffBins)
if depth==maxDepth:
return minError
elif errorBelow <= errorAbove:
x = searchInRangeRec(minError[0]-steps, minError[0], float(steps)/10, depth+1, maxDepth)
return x[0], x[1]
else:
x = searchInRangeRec(minError[0], minError[0]+steps, float(steps)/10, depth+1, maxDepth)
return x[0], x[1]
beta, error = searchInRangeRec(1, 10, 1, 0, 3)
print param, (error/maxBins)
# s = numpy.random.poisson(1.2, 10000)
# s = numpy.random.zipf(1.5, 10000)
# f = 3.0
# s = map(lambda x: (float(x)-(1+(random.random()/f)))*f, s)
# s.extend([0] * (100*60))
#binConditions2.append([lambda x: x>maxBins, ">" + str(maxBins-1) + str("X")])
"""expDistData = map(lambda x: int(x), numpy.random.exponential(beta, 10000))
(("pmcViews", ()), (2011, 6), "PMC views"),
(("maxCitations", ()), (2009, 3), "Citations")
]
attributeNames = map(lambda x: x[0][0], attributeList)
attributePrintNames = map(lambda x: x[2], attributeList)
calls = map(lambda x: x[0], attributeList)
stats = []
for ind, attr in zip(range(0, len(attributeList)), attributeList):
call = attr[0]
lowerBound = attr[1]
attName = attr[0][0]
valuesForMetric = filter(lambda x: x != None, map(lambda doc: applyCall(doc, call),
SimpleDoc.getallBetween(lowerBound, None)
))
minV, maxV, meanV, std = min(valuesForMetric), max(valuesForMetric), np.mean(valuesForMetric), np.std(valuesForMetric)
stats.append((attName, call, meanV, std, len(valuesForMetric)))
print attName + "\t" + "\t".join(map(lambda x: str(x), [minV, maxV, meanV, std]))
statValues = []
for stat in stats:
name = stat[0]
call = stat[1]
mean = stat[2]
std = stat[3]
numValues = stat[4]
valuesForMetric = filter(lambda x: x != None, map(lambda doc: applyCall(doc, call),
SimpleDoc.getallBetween(lowerBound, None)
))
minV, maxV, meanV, std = min(valuesForMetric), max(valuesForMetric), np.mean(valuesForMetric), np.std(valuesForMetric)
print attName + "\t" + "\t".join(map(lambda x: str(x), [minV, maxV, meanV, std]))
"""
cat = "Biological Sciences"
"""consideredDocs = filter(
lambda doc:
doc.mendeleyDisciplines != None and cat in doc.mendeleyDisciplines,
SimpleDoc.getallBetween((2012,6), (2012,8))
)"""
consideredDocs = SimpleDoc.getallBetween((2012,6), (2012,8))
print len(consideredDocs)
matrix = getAttributeValueMatrix(consideredDocs, calls)
corrs = correlationBetweenEverything(matrix, attributeNames)
"""f = open("foo", "w")
for corr in corrs:
f.write(corr.toJson() + "\n")
f.close()"""
# corrs = CorrelationItem.fromFile("stuff/pairwise_corr_2012-6_2012-8.json")
f = open("foo", "w")
m = []
for a1 in attributeNames:
row = []
