def test_01_normalise_doi(self, name, kwargs):
doi_arg = kwargs.get("doi")
prefix_arg = kwargs.get("prefix")
whitespace_arg = kwargs.get("whitespace")
raises_arg = kwargs.get("raises")
raises = EXCEPTIONS.get(raises_arg)
###############################################
## set up
rawDOI = None
if doi_arg != "none":
rawDOI = "10.1234/abc/122"
doi = rawDOI
if prefix_arg not in ["-", "invalid", "none"]:
doi = prefix_arg + doi
elif prefix_arg == "invalid":
doi = "somerubbish" + doi
if whitespace_arg == "yes":
doi = " " + doi + "\t\n"
###########################################################
# Execution
if raises is not None:
with self.assertRaises(raises):
norm = normalise_doi(doi)
else:
norm = normalise_doi(doi)
assert norm == rawDOI
def get_normalised_doi(self):
if self.data.get("index", {}).get("doi") is not None:
return self.data["index"]["doi"]
doi = self.bibjson().get_one_identifier(constants.IDENT_TYPE_DOI)
if doi is None:
return None
try:
return normalise.normalise_doi(doi)
except ValueError:
# can't be normalised, so we just return the doi as-is
return doi
def doi_url(doi):
"""
Create a link from a DOI.
:param doi: the string DOI
:return: the HTML link
"""
try:
return "https://doi.org/" + normalise_doi(doi)
except ValueError:
return ""
def get_normalised_doi(self):
if self.data.get("index", {}).get("doi") is not None:
return self.data["index"]["doi"]
doi = self.bibjson().get_one_identifier(constants.IDENT_TYPE_DOI)
if doi is None:
return None
try:
return normalise.normalise_doi(doi)
except ValueError:
# can't be normalised, so we just return the doi as-is
return doi
def duplicates(cls,
publisher_record_id=None,
doi=None,
fulltexts=None,
title=None,
volume=None,
number=None,
start=None,
should_match=None,
size=10):
# some input sanitisation
urls = fulltexts if isinstance(fulltexts, list) else [
fulltexts
] if isinstance(fulltexts, str) or isinstance(fulltexts, str) else []
# make sure that we're dealing with the normal form of the identifiers
norm_urls = []
for url in urls:
try:
norm = normalise.normalise_url(url)
norm_urls.append(norm)
except ValueError:
# use the non-normal form
norm_urls.append(url)
urls = norm_urls
try:
doi = normalise.normalise_doi(doi)
except ValueError:
# leave the doi as it is
pass
q = DuplicateArticleQuery(publisher_record_id=publisher_record_id,
doi=doi,
urls=urls,
title=title,
volume=volume,
number=number,
start=start,
should_match=should_match,
size=size)
res = cls.query(q=q.query())
return [
cls(**hit.get("_source"))
for hit in res.get("hits", {}).get("hits", [])
]
def _generate_index(self):
# the index fields we are going to generate
issns = []
subjects = []
schema_subjects = []
schema_codes = []
classification = []
langs = []
country = None
licenses = []
publisher = []
classification_paths = []
unpunctitle = None
asciiunpunctitle = None
doi = None
fulltext = None
# the places we're going to get those fields from
cbib = self.bibjson()
jindex = self.data.get('index', {})
hist = self.history()
# get the issns out of the current bibjson
issns += cbib.get_identifiers(cbib.P_ISSN)
issns += cbib.get_identifiers(cbib.E_ISSN)
# get the issn from the journal bibjson
if isinstance(cbib.journal_issns, list):
issns += cbib.journal_issns
# de-duplicate the issns
issns = list(set(issns))
# now get the issns out of the historic records
for date, hbib in hist:
issns += hbib.get_identifiers(hbib.P_ISSN)
issns += hbib.get_identifiers(hbib.E_ISSN)
# get the subjects and concatenate them with their schemes from the current bibjson
for subs in cbib.subjects():
scheme = subs.get("scheme")
term = subs.get("term")
subjects.append(term)
schema_subjects.append(scheme + ":" + term)
classification.append(term)
if "code" in subs:
schema_codes.append(scheme + ":" + subs.get("code"))
# copy the languages
from portality import datasets # delayed import, as it loads some stuff from file
if len(cbib.journal_language) > 0:
langs = [datasets.name_for_lang(l) for l in cbib.journal_language]
# copy the country
if jindex.get('country'):
country = jindex.get('country')
elif cbib.journal_country:
country = xwalk.get_country_name(cbib.journal_country)
# get the title of the license
lic = cbib.get_journal_license()
if lic is not None:
licenses.append(lic.get("title"))
# copy the publisher/provider
if cbib.publisher:
publisher.append(cbib.publisher)
# deduplicate the lists
issns = list(set(issns))
subjects = list(set(subjects))
schema_subjects = list(set(schema_subjects))
classification = list(set(classification))
licenses = list(set(licenses))
publisher = list(set(publisher))
langs = list(set(langs))
schema_codes = list(set(schema_codes))
# work out what the date of publication is
date = cbib.get_publication_date()
# calculate the classification paths
from portality.lcc import lcc # inline import since this hits the database
for subs in cbib.subjects():
scheme = subs.get("scheme")
term = subs.get("term")
if scheme == "LCC":
path = lcc.pathify(term)
if path is not None:
classification_paths.append(path)
# normalise the classification paths, so we only store the longest ones
classification_paths = lcc.longest(classification_paths)
# create an unpunctitle
if cbib.title is not None:
throwlist = string.punctuation + '\n\t'
unpunctitle = "".join(c for c in cbib.title
if c not in throwlist).strip()
try:
asciiunpunctitle = unidecode(unpunctitle)
except:
asciiunpunctitle = unpunctitle
# determine if the seal is applied
has_seal = "Yes" if self.has_seal() else "No"
# create a normalised version of the DOI for deduplication
source_doi = cbib.get_one_identifier(constants.IDENT_TYPE_DOI)
try:
doi = normalise.normalise_doi(source_doi)
except ValueError as e:
# if we can't normalise the DOI, just store it as-is.
doi = source_doi
# create a normalised version of the fulltext URL for deduplication
fulltexts = cbib.get_urls(constants.LINK_TYPE_FULLTEXT)
if len(fulltexts) > 0:
source_fulltext = fulltexts[0]
try:
fulltext = normalise.normalise_url(source_fulltext)
except ValueError as e:
# if we can't normalise the fulltext store it as-is
fulltext = source_fulltext
# build the index part of the object
self.data["index"] = {}
if len(issns) > 0:
self.data["index"]["issn"] = issns
if date != "":
self.data["index"]["date"] = date
self.data["index"][
"date_toc_fv_month"] = date # Duplicated so we can have year/month facets in fv2
if len(subjects) > 0:
self.data["index"]["subject"] = subjects
if len(schema_subjects) > 0:
self.data["index"]["schema_subject"] = schema_subjects
if len(classification) > 0:
self.data["index"]["classification"] = classification
if len(publisher) > 0:
self.data["index"]["publisher"] = publisher
if len(licenses) > 0:
self.data["index"]["license"] = licenses
if len(langs) > 0:
self.data["index"]["language"] = langs
if country is not None:
self.data["index"]["country"] = country
if schema_codes > 0:
self.data["index"]["schema_code"] = schema_codes
if len(classification_paths) > 0:
self.data["index"]["classification_paths"] = classification_paths
if unpunctitle is not None:
self.data["index"]["unpunctitle"] = unpunctitle
if asciiunpunctitle is not None:
self.data["index"]["asciiunpunctitle"] = unpunctitle
if has_seal:
self.data["index"]["has_seal"] = has_seal
if doi is not None:
self.data["index"]["doi"] = doi
if fulltext is not None:
self.data["index"]["fulltext"] = fulltext
def duplicates(cls,
issns=None,
publisher_record_id=None,
doi=None,
fulltexts=None,
title=None,
volume=None,
number=None,
start=None,
should_match=None,
size=10):
# some input sanitisation
issns = issns if isinstance(issns, list) else []
urls = fulltexts if isinstance(
fulltexts, list) else [fulltexts] if isinstance(
fulltexts, str) or isinstance(fulltexts, unicode) else []
# make sure that we're dealing with the normal form of the identifiers
norm_urls = []
for url in urls:
try:
norm = normalise.normalise_url(url)
norm_urls.append(norm)
except ValueError:
# use the non-normal form
norm_urls.append(url)
urls = norm_urls
try:
doi = normalise.normalise_doi(doi)
except ValueError:
# leave the doi as it is
pass
# in order to make sure we don't send too many terms to the ES query, break the issn list down into chunks
terms_limit = app.config.get("ES_TERMS_LIMIT", 1024)
issn_groups = []
lower = 0
upper = terms_limit
while lower < len(issns):
issn_groups.append(issns[lower:upper])
lower = upper
upper = lower + terms_limit
if issns is not None and len(issns) > 0:
duplicate_articles = []
for g in issn_groups:
q = DuplicateArticleQuery(
issns=g,
publisher_record_id=publisher_record_id,
doi=doi,
urls=urls,
title=title,
volume=volume,
number=number,
start=start,
should_match=should_match,
size=size)
# print json.dumps(q.query())
res = cls.query(q=q.query())
duplicate_articles += [
cls(**hit.get("_source"))
for hit in res.get("hits", {}).get("hits", [])
]
return duplicate_articles
else:
q = DuplicateArticleQuery(publisher_record_id=publisher_record_id,
doi=doi,
urls=urls,
title=title,
volume=volume,
number=number,
start=start,
should_match=should_match,
size=size)
# print json.dumps(q.query())
res = cls.query(q=q.query())
return [
cls(**hit.get("_source"))
for hit in res.get("hits", {}).get("hits", [])
]
def _read_match_set(reader, next_row):
n_matches = -1
match_set = MatchSet()
while True:
if next_row is not None:
row = deepcopy(next_row)
next_row = None
else:
try:
row = next(reader)
except StopIteration:
return match_set, None
if row is None:
return match_set, None
a_id = row[0]
root = match_set.root
if root is not None and a_id != root["id"]:
return match_set, row
a_created = row[1]
try:
a_doi = normalise.normalise_doi(row[2])
except:
a_doi = row[2]
try:
a_ft = normalise.normalise_url(row[3])
except:
a_ft = row[3]
a_owner = row[4]
a_issns = row[5]
a_in_doaj = row[6] == "True"
if n_matches != -1:
n_matches = int(row[7])
match_type = row[8]
b_id = row[9]
b_created = row[10]
try:
b_doi = normalise.normalise_doi(row[11])
except:
b_doi = row[11]
try:
b_ft = normalise.normalise_url(row[12])
except:
b_ft = row[12]
b_owner = row[13]
b_issns = row[14]
b_in_doaj = row[15] == "True"
title_match = row[17] == "True"
if root is None:
match_set.add_root(a_id, a_created, a_doi, a_ft, a_owner, a_issns,
a_in_doaj, title_match)
match_set.add_match(b_id, b_created, b_doi, b_ft, b_owner, b_issns,
b_in_doaj, title_match, match_type)
# a catch to make sure that everything is ok with the match set detection
assert n_matches + 1 == len(match_set.matches)
def _generate_index(self):
# the index fields we are going to generate
issns = []
subjects = []
schema_subjects = []
schema_codes = []
classification = []
langs = []
country = None
licenses = []
publisher = []
classification_paths = []
unpunctitle = None
asciiunpunctitle = None
doi = None
fulltext = None
# the places we're going to get those fields from
cbib = self.bibjson()
jindex = self.data.get('index', {})
hist = self.history()
# get the issns out of the current bibjson
issns += cbib.get_identifiers(cbib.P_ISSN)
issns += cbib.get_identifiers(cbib.E_ISSN)
# get the issn from the journal bibjson
if isinstance(cbib.journal_issns, list):
issns += cbib.journal_issns
# de-duplicate the issns
issns = list(set(issns))
# now get the issns out of the historic records
for date, hbib in hist:
issns += hbib.get_identifiers(hbib.P_ISSN)
issns += hbib.get_identifiers(hbib.E_ISSN)
# get the subjects and concatenate them with their schemes from the current bibjson
for subs in cbib.subjects():
scheme = subs.get("scheme")
term = subs.get("term")
subjects.append(term)
schema_subjects.append(scheme + ":" + term)
classification.append(term)
if "code" in subs:
schema_codes.append(scheme + ":" + subs.get("code"))
# copy the languages
from portality import datasets # delayed import, as it loads some stuff from file
if len(cbib.journal_language) > 0:
langs = [datasets.name_for_lang(l) for l in cbib.journal_language]
# copy the country
if jindex.get('country'):
country = jindex.get('country')
elif cbib.journal_country:
country = xwalk.get_country_name(cbib.journal_country)
# get the title of the license
lic = cbib.get_journal_license()
if lic is not None:
licenses.append(lic.get("title"))
# copy the publisher/provider
if cbib.publisher:
publisher.append(cbib.publisher)
# deduplicate the lists
issns = list(set(issns))
subjects = list(set(subjects))
schema_subjects = list(set(schema_subjects))
classification = list(set(classification))
licenses = list(set(licenses))
publisher = list(set(publisher))
langs = list(set(langs))
schema_codes = list(set(schema_codes))
# work out what the date of publication is
date = cbib.get_publication_date()
# calculate the classification paths
from portality.lcc import lcc # inline import since this hits the database
for subs in cbib.subjects():
scheme = subs.get("scheme")
term = subs.get("term")
if scheme == "LCC":
path = lcc.pathify(term)
if path is not None:
classification_paths.append(path)
# normalise the classification paths, so we only store the longest ones
classification_paths = lcc.longest(classification_paths)
# create an unpunctitle
if cbib.title is not None:
throwlist = string.punctuation + '\n\t'
unpunctitle = "".join(c for c in cbib.title if c not in throwlist).strip()
try:
asciiunpunctitle = unidecode(unpunctitle)
except:
asciiunpunctitle = unpunctitle
# determine if the seal is applied
has_seal = "Yes" if self.has_seal() else "No"
# create a normalised version of the DOI for deduplication
source_doi = cbib.get_one_identifier(constants.IDENT_TYPE_DOI)
try:
doi = normalise.normalise_doi(source_doi)
except ValueError as e:
# if we can't normalise the DOI, just store it as-is.
doi = source_doi
# create a normalised version of the fulltext URL for deduplication
fulltexts = cbib.get_urls(constants.LINK_TYPE_FULLTEXT)
if len(fulltexts) > 0:
source_fulltext = fulltexts[0]
try:
fulltext = normalise.normalise_url(source_fulltext)
except ValueError as e:
# if we can't normalise the fulltext store it as-is
fulltext = source_fulltext
# build the index part of the object
self.data["index"] = {}
if len(issns) > 0:
self.data["index"]["issn"] = issns
if date != "":
self.data["index"]["date"] = date
self.data["index"]["date_toc_fv_month"] = date # Duplicated so we can have year/month facets in fv2
if len(subjects) > 0:
self.data["index"]["subject"] = subjects
if len(schema_subjects) > 0:
self.data["index"]["schema_subject"] = schema_subjects
if len(classification) > 0:
self.data["index"]["classification"] = classification
if len(publisher) > 0:
self.data["index"]["publisher"] = publisher
if len(licenses) > 0:
self.data["index"]["license"] = licenses
if len(langs) > 0:
self.data["index"]["language"] = langs
if country is not None:
self.data["index"]["country"] = country
if schema_codes > 0:
self.data["index"]["schema_code"] = schema_codes
if len(classification_paths) > 0:
self.data["index"]["classification_paths"] = classification_paths
if unpunctitle is not None:
self.data["index"]["unpunctitle"] = unpunctitle
if asciiunpunctitle is not None:
self.data["index"]["asciiunpunctitle"] = unpunctitle
if has_seal:
self.data["index"]["has_seal"] = has_seal
if doi is not None:
self.data["index"]["doi"] = doi
if fulltext is not None:
self.data["index"]["fulltext"] = fulltext
def duplicates(cls, issns=None, publisher_record_id=None, doi=None, fulltexts=None, title=None, volume=None, number=None, start=None, should_match=None, size=10):
# some input sanitisation
issns = issns if isinstance(issns, list) else []
urls = fulltexts if isinstance(fulltexts, list) else [fulltexts] if isinstance(fulltexts, str) or isinstance(fulltexts, unicode) else []
# make sure that we're dealing with the normal form of the identifiers
norm_urls = []
for url in urls:
try:
norm = normalise.normalise_url(url)
norm_urls.append(norm)
except ValueError:
# use the non-normal form
norm_urls.append(url)
urls = norm_urls
try:
doi = normalise.normalise_doi(doi)
except ValueError:
# leave the doi as it is
pass
# in order to make sure we don't send too many terms to the ES query, break the issn list down into chunks
terms_limit = app.config.get("ES_TERMS_LIMIT", 1024)
issn_groups = []
lower = 0
upper = terms_limit
while lower < len(issns):
issn_groups.append(issns[lower:upper])
lower = upper
upper = lower + terms_limit
if issns is not None and len(issns) > 0:
duplicate_articles = []
for g in issn_groups:
q = DuplicateArticleQuery(issns=g,
publisher_record_id=publisher_record_id,
doi=doi,
urls=urls,
title=title,
volume=volume,
number=number,
start=start,
should_match=should_match,
size=size)
# print json.dumps(q.query())
res = cls.query(q=q.query())
duplicate_articles += [cls(**hit.get("_source")) for hit in res.get("hits", {}).get("hits", [])]
return duplicate_articles
else:
q = DuplicateArticleQuery(publisher_record_id=publisher_record_id,
doi=doi,
urls=urls,
title=title,
volume=volume,
number=number,
start=start,
should_match=should_match,
size=size)
# print json.dumps(q.query())
res = cls.query(q=q.query())
return [cls(**hit.get("_source")) for hit in res.get("hits", {}).get("hits", [])]
def _read_match_set(reader, next_row):
n_matches = -1
match_set = MatchSet()
while True:
if next_row is not None:
row = deepcopy(next_row)
next_row = None
else:
try:
row = reader.next()
except StopIteration:
return match_set, None
if row is None:
return match_set, None
a_id = row[0]
root = match_set.root
if root is not None and a_id != root["id"]:
return match_set, row
a_created = row[1]
try:
a_doi = normalise.normalise_doi(row[2])
except:
a_doi = row[2]
try:
a_ft = normalise.normalise_url(row[3])
except:
a_ft = row[3]
a_owner = row[4]
a_issns = row[5]
a_in_doaj = row[6] == "True"
if n_matches != -1:
n_matches = int(row[7])
match_type = row[8]
b_id = row[9]
b_created = row[10]
try:
b_doi = normalise.normalise_doi(row[11])
except:
b_doi = row[11]
try:
b_ft = normalise.normalise_url(row[12])
except:
b_ft = row[12]
b_owner = row[13]
b_issns = row[14]
b_in_doaj = row[15] == "True"
title_match = row[17] == "True"
if root is None:
match_set.add_root(a_id, a_created, a_doi, a_ft, a_owner, a_issns, a_in_doaj, title_match)
match_set.add_match(b_id, b_created, b_doi, b_ft, b_owner, b_issns, b_in_doaj, title_match, match_type)
# a catch to make sure that everything is ok with the match set detection
assert n_matches + 1 == len(match_set.matches)
