def __init__(self, inputStr):
from biotool import BioTool
self.bioToolCheck = BioTool(inputStr)
self.semaphore = threading.Semaphore()
def __init__(self, inputStr):
from biotool import BioTool
self.bioToolCheck = BioTool(inputStr)
self.semaphore = threading.Semaphore()
class Result:
hitsNumber = 0
googleHits = 0
wikiHits = 0
pubmedHits = 0
articleList = None
bioToolCheck = None
semaphore = None
def __init__(self, inputStr):
from biotool import BioTool
self.bioToolCheck = BioTool(inputStr)
self.semaphore = threading.Semaphore()
# Calculate a part of final grade using English Wikipedia
# @return: number of articles in english wikipedia
def getWiki(self):
tmp = self.bioToolCheck.get_wikipedia_results_count()
self.semaphore.acquire()
self.wikiHits += tmp
self.semaphore.release()
# Calculate a part of final grade using Google web searcher
# @return: number of articles divided by 1000 in google
def getGoogle(self):
tmp = int(
math.ceil(self.bioToolCheck.get_google_results_count() / 1000.0))
self.semaphore.acquire()
self.googleHits += tmp
self.semaphore.release()
# Method for a single thread processing single author's h-index
def checkSingleAuthor(self, singleAuthor, q):
q.put(singleAuthor.get_h_index())
# Method for a single thread processing single article
def checkSingleArticle(self, singleArticle, i):
# Power of the article itself is multiplied by...
articlePower = 1.0 * (len(self.articleList) - i) / len(
self.articleList)
hIndexSum = 0
q = Queue()
threads = []
for singleAuthor in singleArticle.authors:
tSingle = Thread(target=self.checkSingleAuthor,
args=(singleAuthor, q))
tSingle.start()
threads.append(tSingle)
for oneThread in threads:
oneThread.join()
while (q.empty() != True):
hIndexSum += int(q.get())
# ...a sum of average h-index of all authors and...
avgHIndex = 1.0 * hIndexSum / len(singleArticle.authors)
# ...number of citations.
numOfCitation = singleArticle.get_related_citations_number()
self.semaphore.acquire()
self.pubmedHits += int(
math.ceil(articlePower * (avgHIndex + numOfCitation)))
self.semaphore.release()
# Calculate a part of final grade using Articles from PubMed db
def getFromArticles(self):
self.articleList = self.bioToolCheck.get_articles()
i = 0
threads = []
for singleArticle in self.articleList:
# Considering single article
tSingle = Thread(
target=self.checkSingleArticle,
name=i,
args=(singleArticle, i),
)
i += 1
tSingle.start()
threads.append(tSingle)
for oneThread in threads:
oneThread.join()
# Number of all articles plus sum from all of them
self.semaphore.acquire()
self.pubmedHits += len(self.articleList)
self.semaphore.release()
# Grade is given as:
# Total + Sum(i=0 -> Total-1) [(Total-i)/Total]*(hIndexMean+NumberOfCitations)
# @return: total grade for a tool
def getHits(self):
return self.pubmedHits, self.googleHits, self.wikiHits
class Result:
hitsNumber = 0
googleHits = 0
wikiHits = 0
pubmedHits = 0
articleList = None
bioToolCheck = None
semaphore = None
def __init__(self, inputStr):
from biotool import BioTool
self.bioToolCheck = BioTool(inputStr)
self.semaphore = threading.Semaphore()
# Calculate a part of final grade using English Wikipedia
# @return: number of articles in english wikipedia
def getWiki(self):
tmp = self.bioToolCheck.get_wikipedia_results_count()
self.semaphore.acquire()
self.wikiHits += tmp
self.semaphore.release()
# Calculate a part of final grade using Google web searcher
# @return: number of articles divided by 1000 in google
def getGoogle(self):
tmp = int(math.ceil(self.bioToolCheck.get_google_results_count() / 1000.0))
self.semaphore.acquire()
self.googleHits += tmp
self.semaphore.release()
# Method for a single thread processing single author's h-index
def checkSingleAuthor(self, singleAuthor, q):
q.put(singleAuthor.get_h_index())
# Method for a single thread processing single article
def checkSingleArticle(self, singleArticle, i):
# Power of the article itself is multiplied by...
articlePower = 1.0 * (len(self.articleList) - i) / len(self.articleList)
hIndexSum = 0
q = Queue()
threads = []
for singleAuthor in singleArticle.authors:
tSingle = Thread(target=self.checkSingleAuthor, args=(singleAuthor, q))
tSingle.start()
threads.append(tSingle)
for oneThread in threads:
oneThread.join()
while(q.empty() != True):
hIndexSum += int(q.get())
# ...a sum of average h-index of all authors and...
avgHIndex = 1.0 * hIndexSum / len(singleArticle.authors)
# ...number of citations.
numOfCitation = singleArticle.get_related_citations_number()
self.semaphore.acquire()
self.pubmedHits += int(math.ceil(articlePower * (avgHIndex + numOfCitation)))
self.semaphore.release()
# Calculate a part of final grade using Articles from PubMed db
def getFromArticles(self):
self.articleList = self.bioToolCheck.get_articles()
i = 0
threads = []
for singleArticle in self.articleList:
# Considering single article
tSingle = Thread(target=self.checkSingleArticle, name=i, args=(singleArticle, i),)
i += 1
tSingle.start()
threads.append(tSingle)
for oneThread in threads:
oneThread.join()
# Number of all articles plus sum from all of them
self.semaphore.acquire()
self.pubmedHits += len(self.articleList)
self.semaphore.release()
# Grade is given as:
# Total + Sum(i=0 -> Total-1) [(Total-i)/Total]*(hIndexMean+NumberOfCitations)
# @return: total grade for a tool
def getHits(self):
return self.pubmedHits, self.googleHits, self.wikiHits