def get_by_eid(self, eid):
"""Return the h-index of an author by a EID if found, None otherwise.
"""
au = AuthorSearch('AU-ID({})'.format(eid))
if au.get_results_size() == 0:
return None
assert au.get_results_size() == 1
res = AuthorRetrieval(au.authors[0][0])
return res.h_index
def get_by_name(self, first, last):
"""Return a table of EID, affiliation, town, country otherwise."""
au = AuthorSearch("AUTHLAST({}) and AUTHFIRST({})".format(last, first))
if au.get_results_size() == 0:
return None
df = pd.DataFrame(au.authors)
ret = []
for x in zip(df["eid"], df["affiliation"], df["city"], df["country"]):
tokens = x[0].split("-")
ret.append([tokens[-1], x[1], x[2], x[3]])
return ret
def test_authors_in_cache():
create_cache(drop=True, file=test_cache)
# Variables
expected_auth = ["53164702100", "57197093438"]
search_auth = ["55317901900"]
# Test empty cache
df1 = pd.DataFrame(expected_auth, columns=["auth_id"], dtype="int64")
incache, tosearch = authors_in_cache(df1, file=test_cache)
expected_cols = ['auth_id', 'eid', 'surname', 'initials', 'givenname',
'affiliation', 'documents', 'affiliation_id', 'city',
'country', 'areas']
expected_auth = [int(au) for au in expected_auth]
assert_equal(tosearch, expected_auth)
assert_equal(len(incache), 0)
assert_equal(incache.columns.tolist(), expected_cols)
# Test partial retrieval
q = "AU-ID({})".format(') OR AU-ID('.join([str(a) for a in expected_auth]))
res = pd.DataFrame(AuthorSearch(q).authors, dtype="int64")
res["auth_id"] = res["eid"].str.split("-").str[-1]
res = res[expected_cols]
cache_insert(res, table="authors", file=test_cache)
df2 = pd.DataFrame(expected_auth + search_auth, columns=["auth_id"],
dtype="int64")
incache, tosearch = authors_in_cache(df2, file=test_cache)
assert_equal(tosearch, [55317901900])
assert_equal(len(incache), 2)
# Test full retrieval
incache, tosearch = authors_in_cache(df1, file=test_cache)
assert_equal(tosearch, [])
assert_equal(len(incache), 2)
def create_obj(params):
if q_type == "author":
return AuthorSearch(**params)
elif q_type == "docs":
params["integrity_fields"] = fields
params["view"] = view
return ScopusSearch(**params)
def get_by_name(self, first, last):
"""Return the h-index of an author if there is only one matching, None if none is
found, or a table with EID, affiliation, town, country otherwise.
"""
au = AuthorSearch('AUTHLAST({}) and AUTHFIRST({})'.format(last, first))
if au.get_results_size() == 0:
return [None, False]
elif au.get_results_size() == 1:
res = AuthorRetrieval(au.authors[0][0])
return [res.h_index, False]
else:
df = pd.DataFrame(au.authors)
ret = []
for x in zip(df['eid'], df['affiliation'], df['city'],
df['country']):
tokens = x[0].split('-')
ret.append([tokens[-1], x[1], x[2], x[3]])
return [ret, True]
def test_retrieve_authors_insert():
make_database(test_cache, drop=True)
conn = connect_database(test_cache)
# Variables
expected_auth = [53164702100, 57197093438]
search_auth = [55317901900]
expected_cols = [
'auth_id', 'eid', 'surname', 'initials', 'givenname', 'affiliation',
'documents', 'affiliation_id', 'city', 'country', 'areas'
]
# Insert data
q = f"AU-ID({robust_join(expected_auth, sep=') OR AU-ID(')})"
res = pd.DataFrame(AuthorSearch(q, refresh=refresh).authors, dtype="int64")
res["auth_id"] = res["eid"].str.split("-").str[-1]
res = res[expected_cols]
insert_data(res, conn, table="authors")
# Retrieve data
df = pd.DataFrame(expected_auth + search_auth,
columns=["auth_id"],
dtype="int64")
incache, missing = retrieve_authors(df, conn)
assert_equal(incache.shape[0], 2)
assert_equal(missing, [55317901900])
def search_data(self):
pdb.set_trace()
s = AuthorSearch('AUTHLAST(Selten) and AUTHFIRST(Reinhard)',
refresh=True)
pdb.set_trace()
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Tests for `scopus.AuthorSearch` module."""
from collections import namedtuple
from nose.tools import assert_equal, assert_true
from pybliometrics.scopus import AuthorSearch
s1 = AuthorSearch('authlast(selten) and authfirst(reinhard)', refresh=True)
s2 = AuthorSearch('authlast(selten)', download=False)
def test_authors():
order = 'eid surname initials givenname affiliation documents '\
'affiliation_id city country areas'
Author = namedtuple('Author', order)
expected = [
Author(eid='9-s2.0-6602907525',
surname='Selten',
initials='R.',
givenname='Reinhard',
affiliation='Universität Bonn',
documents='74',
affiliation_id='60007493',
city='Bonn',
country='Germany',
areas='ECON (73); MATH (19); BUSI (16)')
]
assert_equal(s1.authors, expected)
def base_query(q_type, query, refresh=False, fields=None, size_only=False):
"""Wrapper function to perform a particular search query.
Parameters
----------
q_type : str
Determines the query search that will be used. Allowed values:
"author", "docs".
query : str
The query string.
refresh : bool (optional, default=False)
Whether to refresh cached files if they exist, or not.
fields : list of field names (optional, default=None)
Fields in the Scopus query that must always present. To be passed
onto pybliometrics.scopus.ScopusSearch. Will be ignored
when q_type = "author".
size_only : bool (optional, default=False)
Whether to not download results and return the number
of results instead.
tsleep: float
Seconds to wait in case of failure due to errors.
Returns
-------
res : list of namedtuples (if size_only is False) or int
Documents represented by namedtuples as returned from scopus or
number of search results.
Raises
------
ValueError:
If q_type is none of the allowed values.
"""
params = {"query": query, "refresh": refresh, "download": not size_only}
# Download query until server is available
try:
if q_type == "author":
obj = AuthorSearch(**params)
elif q_type == "docs":
params["integrity_fields"] = fields
obj = ScopusSearch(**params)
except (AttributeError, Scopus500Error, KeyError, HTTPError):
# exception of all errors here has to be maintained due to the
# occurrence of not replicable errors (e.g. 'cursor', HTTPError)
sleep(2.0)
return base_query(q_type,
query,
refresh=True,
fields=None,
size_only=size_only)
if size_only:
return obj.get_results_size()
# Parse results, refresh once if integrity check fails or when server
# sends bad results (in this case pause querying for a while)
try:
if q_type == "author":
res = obj.authors or []
elif q_type == "docs":
res = obj.results or []
except (AttributeError, Scopus500Error, KeyError, HTTPError):
# exception of all errors here has to be maintained due to the
# occurrence of not replicable errors (e.g. 'cursor', HTTPError)
return base_query(q_type,
query,
refresh=True,
fields=None,
size_only=size_only)
return res
def base_query(q_type,
query,
refresh=False,
view="COMPLETE",
fields=None,
size_only=False):
"""Wrapper function to perform a particular search query.
Parameters
----------
q_type : str
Determines the query search that will be used. Allowed values:
"author", "docs".
query : str
The query string.
refresh : bool (optional, default=False)
Whether to refresh cached files if they exist, or not.
fields : list of field names (optional, default=None)
Fields in the Scopus query that must always present. To be passed
onto pybliometrics.scopus.ScopusSearch. Will be ignored
when q_type = "author".
size_only : bool (optional, default=False)
Whether to not download results and return the number
of results instead.
Returns
-------
res : list of namedtuples (if size_only is False) or int
Documents represented by namedtuples as returned from scopus or
number of search results.
Raises
------
ValueError:
If q_type is none of the allowed values.
"""
from pybliometrics.scopus import AuthorSearch, ScopusSearch
params = {"query": query, "refresh": refresh, "download": not size_only}
if q_type == "author":
au = AuthorSearch(**params)
if size_only:
return au.get_results_size()
else:
return au.authors or []
elif q_type == "docs":
params["integrity_fields"] = fields
params["view"] = view
if size_only:
return ScopusSearch(**params).get_results_size()
try:
return ScopusSearch(**params).results or []
except AttributeError:
params.pop("integrity_fields")
params["refresh"] = True
return ScopusSearch(**params).results or []
names = []
largest_name = 0
largest_title = 0
with open(args.input, "r") as infile:
for line in infile:
tokens = line.rstrip().split(";")
if len(tokens) == 4 and tokens[3]:
initials = []
for firstname in tokens[1].split(" "):
initials.append(firstname[0] + ".")
hindex = 0
if args.fake:
hindex = int(random.expovariate(1 / 20.0))
else:
au = AuthorSearch(f"AU-ID({tokens[3]})")
if au.get_results_size() > 0:
assert au.get_results_size() == 1
hindex = int(
AuthorRetrieval(au.authors[0][0]).h_index)
fullname = tokens[0] + " " + " ".join(initials)
largest_name = max(largest_name, len(fullname))
largest_title = max(largest_title, len(tokens[2]))
names.append([fullname, tokens[2], hindex])
for name in sorted(names, key=itemgetter(2), reverse=True):
print(
f'{name[0] + " " * (largest_name - len(name[0]))} ({name[1]}) {" " * (largest_title - len(name[1]))} {"▇" * name[2]} {name[2]}'
)
with open('cis_academics.csv', 'r') as readFile:
reader = csv.reader(readFile, delimiter=',')
for row in reader:
if first_line_flag:
first_line_flag = False
continue
given_name = row[0]
family_name = row[1]
orcid_id = row[2]
print(family_name + " " + given_name)
if len(orcid_id) > 1:
print("orcid:" + orcid_id)
authorSearch = AuthorSearch('ORCID(' + orcid_id + ')')
authors = authorSearch.authors
if authors == None:
print("no result with orcid!")
authorSearch = AuthorSearch('AUTHLAST(' + family_name +
') and AUTHFIRST(' + given_name +
') and AFFIL(University)')
authors = authorSearch.authors
if authors == None:
print("no result with first")
authorSearch = AuthorSearch('AUTHLAST(' + given_name +
') and AUTHFIRST(' + family_name +
') and AFFIL(University)')
authors = authorSearch.authors