def query_scholarly(self, author = None, keyword = None, pub = None):
import scholarly;
RESULTS = {}
if author is not None and keyword is not None and pub is not None:
RESULTS['author'] = scholarly.search_author(author);
RESULTS['keyword'] = scholarly.search_keyword(keyword);
RESULTS['pub'] = scholarly.search_pubs_query(pub);
return RESULTS;
elif author is not None:
return scholarly.search_author(author);
elif keyword is not None:
return scholarly.search_keyword(keyword);
elif pub is not None:
return scholarly.search_pubs_query(pub);
def publication_information(pub):
p = ""
c = 0
dat = ""
publisher = ""
search_query = scholarly.search_pubs_query(pub)
# try:
publication = next(search_query).fill()
if re.search(publication.bib["ENTRYTYPE"], "article"):
try:
dat = publication.bib["year"]
except:
dat = ""
print(dat)
try:
p = publication.bib["journal"]
except:
p = ""
print(p)
try:
c = publication.citedby
except:
c = 0
print(c)
try:
publisher = publication.bib["publisher"]
except:
publisher = 0
print(publisher)
# except:
# pass
return p, c, dat, publisher
def scholarly_query(authors, title):
"""
Query Google Scholar database.
Args:
authors (list): a list of strings for up the first authors last names.
title (str): the title of the article.
Returns:
A record (dict) of the bibtex entry obtained from Google Scholar.
"""
query = ' '.join(authors) + ' ' + title
search_query = scholarly.search_pubs_query(query)
try:
res = next(search_query)
except StopIteration:
return None
res.fill()
if 'abstract' in res.bib:
del res.bib['abstract']
# Post-process title
res.bib['title'] = re.sub('\\.*$', '', res.bib['title'])
print('S: ' + nomenclature.gen_filename(res.bib))
return res.bib
def query_scholar(self):
"""
QUERT ARTICLES FROM GOOGLE SCHOLAR
Parameters:
------
None
Function scrap Google Scholar and push articles in MongoDB
Return
------
None
"""
tab = self.get_db_object()
indice = 0
while indice < len(tab):
if 'ngc' in tab[indice]:
search_query = scholarly.search_pubs_query(tab[indice]['ngc'])
for i in range(500):
current_article = next(search_query)
current_article = current_article.__dict__
current_article["biblio"] = current_article.pop('bib')
current_article["ngc"] = tab[indice]["ngc"]
current_article["_object_id"] = tab[indice]["_id"]
print(current_article)
articles.insert(current_article)
indice += 1
indice += 1
def get_all_studies(keywords: List[str]) -> List[Study]:
retval = []
shuffle(keywords)
shuffled = ' '.join(keywords)
print(shuffled)
query = scholarly.search_pubs_query(shuffled)
#
# for i in range(10):
# try:
# res = next(query)
# retval.append(Study(res.bib['title'], res.bib['url'], res.bib['author']))
# except KeyError:
# retval.append(Study(res.bib['title'], "", res.bib['author']))
# except StopIteration:
# pass
while True:
try:
res = next(query)
retval.append(Study(res.bib['title'], res.bib['url'], res.bib['author']))
except KeyError:
retval.append(Study(res.bib['title'], "", res.bib['author']))
except StopIteration:
break
return retval
def rename_bib_file(citeDir, filename):
my_bucket, s3_resource = aws_bucket_info()
bibtex_file = my_bucket.Object(filename).get()['Body'].read().decode(
'utf-8')
parser = BibTexParser()
parser.customization = convert_to_unicode
bibtex_database = bibtexparser.loads(bibtex_file, parser=parser)
entries = bibtex_database.entries[0]
df = read_bib_df(citation_pickle_file=citation_pickle_file)
if entries['ID'] not in df['ID'].values:
search_query = scholarly.search_pubs_query(entries['title'])
res = next(search_query)
strings_to_look = ['url', 'abstract']
search_output = []
for ss in strings_to_look:
try:
search_output.append(res.bib[ss])
except:
search_output.append("")
dict_to_store = {
'ID': entries['ID'],
'numLikes': 0,
'Title': entries['title'],
'Authors': entries['author'],
'url': search_output[0],
'Abstract': search_output[1]
}
store_bib_in_df(dict_to_store,
citation_pickle_file=citation_pickle_file)
new_bibfile = citeDir + entries['ID'] + "_slrm.bib"
return new_bibfile
else:
return ""
def get_cite_count_by_doi(doi):
query = scholarly.search_pubs_query(doi)
try:
result = next(query).fill()
return (result.citedby) # Return citedby count
except:
return (-1) # Couldn't find doi via scholar
def search_scholar(self, query, n_results):
""" Returns a list containing the n_results most relevant
Publications to the keywords.
Parameters
----------
query: string
Query to search on Google Scholar.
n_results: int
Number of articles to return.
Returns
-------
results: list(Publications)
List containing Publication objects.
"""
generator_results = scholarly.search_pubs_query(query)
self.results_scholar = [next(generator_results)
for _ in range(n_results)]
# time.sleep(1)
# for _ in range(n_results):
# res = next(generator_results).fill()
# time.sleep(1)
# self.results_scholar.append(res)
return self.results_scholar
def scholarly_query(authors, title):
"""
Query Google Scholar database.
Args:
authors (list): a list of strings for up the first authors last names.
title (str): the title of the article.
Returns:
A record (dict) of the bibtex entry obtained from Google Scholar.
"""
query = ' '.join(authors) + ' ' + title
search_query = scholarly.search_pubs_query(query)
try:
res = next(search_query)
except StopIteration:
return None
res.fill()
if 'abstract' in res.bib:
del res.bib['abstract']
# Post-process title
res.bib['title'] = re.sub('\\.*$', '', res.bib['title'])
print('S: ' + nomenclature.gen_filename(res.bib))
return res.bib
def fetch_bibtex_by_fulltext_scholar(txt, assess_results=True):
import scholarly
scholarly._get_page = _get_page_fast # remove waiting time
logger.debug(txt)
search_query = scholarly.search_pubs_query(txt)
# get the most likely match of the first results
results = list(search_query)
if len(results) > 1 and assess_results:
maxscore = 0
result = results[0]
for res in results:
score = _scholar_score(txt, res.bib)
if score > maxscore:
maxscore = score
result = res
else:
result = results[0]
# use url_scholarbib to get bibtex from google
if getattr(result, 'url_scholarbib', ''):
bibtex = scholarly._get_page(result.url_scholarbib).strip()
else:
raise NotImplementedError(
'no bibtex import linke. Make crossref request using title?')
return bibtex
def test_get_cited_by(self):
pub = next(
scholarly.search_pubs_query(
'frequency-domain analysis of haptic gratings cholewiak')
).fill()
cites = [c for c in pub.get_citedby()]
self.assertEqual(len(cites), pub.citedby)
def information(pub):
search_query = scholarly.search_pubs_query(pub)
p=next(search_query)
print(p.bib["author"])
return p.bib["author"]
#information("TOWARDS SEAMLESS TRACKING-FREE WEB: IMPROVED DETECTION OF TRACKERS VIA ONE-CLASS LEARNING")
def scholar_view(request, nodeType, nodeLabel):
# Call scholarly library to retrieve Google Scholar information
mostCited = None
# If request is for an Author
if nodeType == "Author":
try:
query = scholarly.search_author(nodeLabel)
result = next(query).fill()
mostCited = result.publications[0].fill()
# If Author cannot be found
except Exception as e:
result = "Sorry, we could not find this author's profile."
nodeType = "Bad query"
mostCited = None
# If request is for a Publicaiton
elif nodeType == "Publication":
try:
query = scholarly.search_pubs_query(nodeLabel)
result = next(query)
print(result)
# If Publication cannot be found
except Exception as e:
result = "Sorry, we could not find this paper's profile."
nodeType = "Bad query"
return render(request, "external/google_search_results.html", {
"result": result,
"nodeType": nodeType,
"mostCited": mostCited
})
def build_super_dict(query, path, amount):
amount = int(amount)
publications = scholarly.search_pubs_query("darwin")
field_names = ["title", "eprint", "author", "abstract", "url"]
counter = 0
with open(
'/home/fabian/Documents/repos/scholar_web_scrapper/results/scrapping_results.csv',
'w') as file:
csv.register_dialect("toMYSQL",
delimiter=";",
quoting=1,
doublequote=1)
writer = csv.DictWriter(file,
fieldnames=field_names,
dialect="toMYSQL")
writer.writeheader()
for publication in publications:
row = publication.__getattribute__('bib')
clean_row = process_row(row)
print(clean_row)
if clean_row is not None:
writer.writerow(clean_row)
if counter >= 15:
break
else:
counter = counter + 1
def test_multiple_publications(self):
''' As of November 18, 2016 there are 12 pubs that fit the search term'''
pubs = [
p.bib['title']
for p in scholarly.search_pubs_query('cholewiak campbell robson')
]
self.assertEqual(len(pubs), 12)
self.assertIn(u'A frequency-domain analysis of haptic gratings', pubs)
def getCitation(titleLine, authorLine):
global citationDict
search_query = scholarly.search_pubs_query(titleLine)
citeCount = next(search_query).citedby
print(titleLine + str(citeCount) + "\n" + authorLine)
return
def captcha_test():
try:
results = scholarly.search_pubs_query('Einstien')
first_result = results.next()
first_result.citedby
except StopIteration:
return True
return False
def doQuery():
# works = Works()
# w1 = works.query(title='zika', author='johannes', publisher_name='Wiley-Blackwell')
# for item in w1:
# print(item['title'])
search_query = scholarly.search_pubs_query('wearable')
print(next(search_query))
def search_pub(query):
search = scholar.search_pubs_query(query)
try:
result = next(iter(search))
result = serialize_result(result)
except StopIteration:
result = {}
return result
def publication_information_2(pub):
publication = {}
try:
search_query_1 = scholarly.search_pubs_query(pub)
publication = next(search_query_1).fill()
print(publication)
except:
print(publication)
def crawl_scholar_paper(title):
cited_by = -1
result = next(scholarly.search_pubs_query(title), None)
if result is not None:
if hasattr(result, 'citedby'):
cited_by = result.citedby
return cited_by
def test_pop():
import matplotlib.pyplot as plt
G = nx.DiGraph()
search_query = sch.search_pubs_query('10.1109/THS.2013.6698999')
P = search_query.next()
P = P.fill()
populate_graph(P, G)
nx.draw_spectral(G)
plt.show()
def gscholar_craw(file_name):
search_query = scholarly.search_pubs_query(file_name)
with open('./gs_data/' + file_name + '.txt', 'w') as f:
for i in range(0, 200):
temp = next(search_query)
dict = temp.bib
json.dump(dict, f)
f.write('\n')
f.close()
def test_multiple_publications(self):
''' As of October 11, 2017 there are 23 pubs that fit the search term'''
pubs = [
p.bib['title'] for p in scholarly.search_pubs_query(
'"naive physics" stability "3d shape"')
]
self.assertEqual(len(pubs), 23)
self.assertIn(
u'Visual perception of the physical stability of asymmetric three-dimensional objects',
pubs)
def trx_searchciters(m):
TRX = MaltegoTransform()
title = m.getProperty("title.article")
title = unidecode(title)
# print title
DOI = m.getProperty("DOI")
if DOI:
query = DOI
else:
query = title
search_query = scholarly.search_pubs_query(query)
try:
result = next(search_query)
except StopIteration:
TRX.addUIMessage("""The DOI could not be found on Google Scholar,
which very likely means Google Scholar has never heard of this article before"""
)
return TRX.returnOutput()
titlemaybe = result.bib['title']
TRX.addUIMessage(
"""Title found: %s.
If this is not what you were looking for, add the article's DOI and search again"""
% make_unicode(clean_obsession(titlemaybe)), UIM_INFORM)
limit = m.slider
count = 0
for citation in result.get_citedby():
if count == limit:
break
for i in bastardi:
title = citation.bib['title'].replace(i, '')
new = TRX.addEntity("me.Article", title.encode('utf-8'))
# new.setLinkLabel('Cited by')
# new.setLinkColor('blue')
# new.setLinkThickness(2)
authors = '; '.join(
[authore for authore in citation.bib['author'].split(' and ')])
for i in bastardi:
authors = authors.replace(i, '')
new.addProperty("author", "Author", "loose", authors.encode('utf-8'))
count += 1
logging(TRX.returnOutput(), m.Maltegoxml)
return TRX.returnOutput()
def test_publication_contents(self):
pub = next(scholarly.search_pubs_query('A frequency-domain analysis of haptic gratings')).fill()
self.assertTrue(pub.bib['author'] == u'Cholewiak, Steven A and Kim, Kwangtaek and Tan, Hong Z and Adelstein, Bernard D')
self.assertTrue(pub.bib['journal'] == u'Haptics, IEEE Transactions on')
self.assertTrue(pub.bib['number'] == u'1')
self.assertTrue(pub.bib['pages'] == u'3--14')
self.assertTrue(pub.bib['publisher'] == u'IEEE')
self.assertTrue(pub.bib['title'] == u'A frequency-domain analysis of haptic gratings')
self.assertTrue(pub.bib['url'] == u'http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5210096')
self.assertTrue(pub.bib['volume'] == u'3')
self.assertTrue(pub.bib['year'] == u'2010')
def test_publication_contents(self):
pub = next(scholarly.search_pubs_query('Creating correct blur and its effect on accommodation')).fill()
self.assertTrue(pub.bib['author'] == u'Cholewiak, Steven A and Love, Gordon D and Banks, Martin S')
self.assertTrue(pub.bib['journal'] == u'Journal of vision')
self.assertTrue(pub.bib['number'] == u'9')
self.assertTrue(pub.bib['pages'] == u'1--1')
self.assertTrue(pub.bib['publisher'] == u'The Association for Research in Vision and Ophthalmology')
self.assertTrue(pub.bib['title'] == u'Creating correct blur and its effect on accommodation')
self.assertTrue(pub.bib['url'] == u'https://jov.arvojournals.org/article.aspx?articleid=2701817')
self.assertTrue(pub.bib['volume'] == u'18')
self.assertTrue(pub.bib['year'] == u'2018')
def GetCitation(title: str, formatStr: str, bibFolder: str) -> str:
'''
Given the title of an article use the package Scholarly to query google
scholar for that article and return the reference file in the format
specified by the format string (currently only .ris is implemented). Write
the ris file to the bibFolder location.
Parameters
----------
title : str
Title of the article.
formatStr : str
Identifier of the filetype for the reference (only ris is implemented).
bibFolder : str
Folder to write the reference file to.
Returns
-------
str
Returns the text of the reference file.
'''
# Currently only supports reference manager format
formats = {"rm": ".ris"}
query = scholarly.search_pubs_query("\"" + title + "\"")
# Gets the link to the citation file
try:
citationLink = next(query).url_scholarbib
except StopIteration:
return ""
citationLink = citationLink.split('=')
# Adjusts the format to the requested one by changing the last query param
citationLink[-1] = formatStr
citationLink = "=".join(citationLink)
# Makes a get request to download the reference
citationText = requests.get(citationLink).text
# Filter out any problematic characters
citationText = re.sub(r'[^\x00-\x7f]', r' ', citationText)
# Writes the citation file to the bibliography folder using the title as the file name7
with open(bibFolder + "/" + title + formats[formatStr],
"w+",
encoding='utf-8') as citationFile:
citationFile.write(citationText)
return citationText
def print_title(query):
'''Print titles of related query publications
'''
print(
"===================================================================")
print("= Titles from ", query)
print(
"===================================================================")
search_query = sc.search_pubs_query(query)
for i in range(5):
paper = next(search_query)
print(paper.bib['title'])
def get_publication(bib):
title = bib['title']
if title not in publications:
try:
search_query = scholarly.search_pubs_query(title)
publication = next(search_query).fill()
publications[title] = publication.bib
except:
print('Cannot find publication: ' + title)
publications[title] = bib
return publications[title]
def main():
search_query = "bert nlp"
print("Fetching paper matching: %s" % search_query)
results = scholarly.search_pubs_query(search_query)
paper = next(results)
paper_data = paper_extraction.extract(paper, verbose=True)
file_utility.save_json(paper_data, PAPER_DATA_PATH)
embed()
def generate_metadata(input_file):
res = None
if "paper" in input_file:
print(input_file)
try:
res = next(scholarly.search_pubs_query(input_file.replace(".pdf","")))
except StopIteration:
print("",end="",sep="")
finally:
res = res.bib
return res
else:
def test_publication_contents(self):
pub = next(scholarly.search_pubs_query('A frequency-domain analysis of haptic gratings')).fill()
superset = pub.bib
subset = {u'author': u'Cholewiak, Steven and Kim, Kwangtaek and Tan, Hong Z and Adelstein, Bernard D and others',
u'journal': u'Haptics, IEEE Transactions on',
u'number': u'1',
u'pages': u'3--14',
u'publisher': u'IEEE',
u'title': u'A frequency-domain analysis of haptic gratings',
u'url': u'http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5210096',
u'volume': u'3',
u'year': u'2010'}
self.assertTrue(all(item in superset.items() for item in subset.items()))
def search_scholar(self):
search_query = scholarly.search_pubs_query(self.term)
self.result = next(search_query).fill()
return self.result
def test_multiple_publications(self):
''' As of October 21, 2015 there are 7 pubs that fit the search term'''
pubs = [p.bib['title'] for p in scholarly.search_pubs_query('cholewiak campbell robson')]
self.assertEqual(len(pubs), 7)
self.assertIn(u'A frequency-domain analysis of haptic gratings', pubs)
def test_get_cited_by(self):
pub = next(scholarly.search_pubs_query('frequency-domain analysis of haptic gratings cholewiak')).fill()
cites = [c for c in pub.get_citedby()]
self.assertEqual(len(cites), pub.citedby)
def test_empty_publication(self):
pubs = [p for p in scholarly.search_pubs_query('')]
self.assertIs(len(pubs), 0)
__author__ = 'fccoelho'
import scholarly
import pymongo
import time
import json
conn = pymongo.MongoClient()
search_query = scholarly.search_pubs_query('zika zikv -author:zika')
def continuous_fetch():
downloaded = [a['url_scholarbib'] for a in conn.scholar.articles.find({}, {'url_scholarbib': 1})]
while True:
doc = {}
# try:
art = next(search_query)
if art.url_scholarbib in downloaded:
continue
if not art._filled:
art.fill()
doc['bib'] = art.bib
try:
doc['citedby'] = art.citedby
except AttributeError:
doc['citedby'] = 0
try:
doc['id_scholarcitedby'] = art.id_scholarcitedby
except AttributeError:
