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 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()
(("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]
# 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()"""
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 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()
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 = []
