def _fuzzy_custom_query(issn, titles):
"""
Este metodo constroi a lista de filtros por título de periódico que
será aplicada na pesquisa boleana como match por similaridade "should".
A lista de filtros é coletada do template de pesquisa customizada
do periódico, quanto este template existir.
"""
custom_queries = journal_titles.load(issn).get('should', [])
titles = [{'title': i} for i in titles if i not in [x['title'] for x in custom_queries]]
titles.extend(custom_queries)
for item in titles:
if len(item['title'].strip()) == 0:
continue
query = {
"fuzzy": {
"reference_source_cleaned": {
"value": utils.cleanup_string(item['title']),
"fuzziness": item.get('fuzziness', 3),
"max_expansions": 50
}
}
}
yield query
def bibliometrics_journal_cited_and_citing_years_heat_web(request):
data = request.data_manager
data['page'] = 'bibliometrics'
titles = request.GET.get('titles', None)
citing_year = request.GET.get('citing_year', None)
cited_year = request.GET.get('cited_year', None)
titles = titles.split('||') if titles else []
if data['selected_journal_code']:
journal = request.stats.articlemeta.journal(code=data['selected_journal_code'])
titles.append(journal.title)
titles.append(journal.abbreviated_title)
titles.extend(x['title'] for x in journal_titles.load(data['selected_journal_code']).get('should', []) if x['title'] not in titles)
data['blist'] = {}
data['titles'] = []
if titles and not len(titles) == 0:
forms = set([i.strip() for i in titles if i])
data['titles'] = u'||'.join(forms)
data['citing_list'] = []
if citing_year and cited_year:
citing_list = request.stats.bibliometrics.cited_and_citing_years_document_list(
data['selected_journal_code'],
titles,
citing_year=citing_year,
cited_year=cited_year
)
data['citing_list'] = citing_list.get('citing_list', 0)
return data
def index(request):
data = request.data_manager
data['page'] = 'home'
data['h5m5'] = request.stats.bibliometrics.google_h5m5(
data['selected_journal_code'])
titles = request.GET.get('titles', None)
titles = titles.split('||') if titles else []
if data['selected_journal_code']:
journal = request.stats.articlemeta.journal(
code=data['selected_journal_code'])
titles.append(journal.title)
titles.append(journal.abbreviated_title)
titles.extend(x['title'] for x in journal_titles.load(
data['selected_journal_code']).get('should', [])
if x['title'] not in titles)
data['titles'] = []
if titles and not len(titles) == 0:
forms = set([i.strip() for i in titles if i])
data['titles'] = u'||'.join(forms)
return data
def bibliometrics_list_received(request):
data = request.data_manager
data['page'] = 'bibliometrics'
titles = request.GET.get('titles', None)
titles = titles.split('||') if titles else []
if data['selected_journal_code']:
journal = request.stats.articlemeta.journal(
code=data['selected_journal_code'])
titles.append(journal.title)
titles.append(journal.abbreviated_title)
titles.extend(x['title'] for x in journal_titles.load(
data['selected_journal_code']).get('should', [])
if x['title'] not in titles)
data['blist'] = []
data['titles'] = []
if titles and not len(titles) == 0:
forms = set([i.strip() for i in titles if i])
data['blist'] = request.stats.bibliometrics.received_citations(
data['selected_journal_code'], forms, py_range=data['py_range'])
data['titles'] = u'||'.join(forms)
return data
def bibliometrics_journal_received_self_and_granted_citation_chart(request):
data = request.data_manager
titles = request.GET.get('titles', None)
titles = titles.split('||') if titles else []
if data['selected_journal_code']:
journal = request.stats.articlemeta.journal(code=data['selected_journal_code'])
titles.append(journal.title)
titles.append(journal.abbreviated_title)
titles.extend(x['title'] for x in journal_titles.load(data['selected_journal_code']).get('should', []) if x['title'] not in titles)
data = request.stats.received_self_and_granted_citation_chart(data['selected_journal_code'], data['selected_collection_code'], titles, py_range=data['py_range'])
return request.chartsconfig.bibliometrics_journal_received_self_and_granted_citation_chart(data)
def bibliometrics_journal_impact_factor_chart(request):
data = request.data_manager
titles = request.GET.get('titles', None)
titles = titles.split('||') if titles else []
if data['selected_journal_code']:
journal = request.stats.articlemeta.journal(code=data['selected_journal_code'])
titles.append(journal.title)
titles.append(journal.abbreviated_title)
titles.extend(x['title'] for x in journal_titles.load(data['selected_journal_code']).get('should', []) if x['title'] not in titles)
data = request.stats.impact_factor_chart(data['selected_journal_code'], data['selected_collection_code'], titles, py_range=data['py_range'])
return request.chartsconfig.bibliometrics_impact_factor(data)
def bibliometrics_journal_cited_and_citing_years_heat(request):
data = request.data_manager
titles = request.GET.get('titles', None)
titles = titles.split('||') if titles else []
if data['selected_journal_code']:
journal = request.stats.articlemeta.journal(code=data['selected_journal_code'])
titles.append(journal.title)
titles.append(journal.abbreviated_title)
titles.extend(x['title'] for x in journal_titles.load(data['selected_journal_code']).get('should', []) if x['title'] not in titles)
data = request.stats.bibliometrics.cited_and_citing_years_heat(
data['selected_journal_code'],
titles
)
return request.chartsconfig.bibliometrics_cited_and_citing_years_heat(data)
def bibliometrics_journal_cited_and_citing_years_heat(request):
data = request.data_manager
titles = request.GET.get('titles', None)
titles = titles.split('||') if titles else []
if data['selected_journal_code']:
journal = request.stats.articlemeta.journal(code=data['selected_journal_code'])
titles.append(journal.title)
titles.append(journal.abbreviated_title)
titles.extend(x['title'] for x in journal_titles.load(data['selected_journal_code']).get('should', []) if x['title'] not in titles)
data = request.stats.bibliometrics.cited_and_citing_years_heat(
data['selected_journal_code'],
titles
)
return request.chartsconfig.bibliometrics_cited_and_citing_years_heat(data)
def _must_not_custom_query(issn):
"""
Este metodo constroi a lista de filtros por título de periódico que
será aplicada na pesquisa boleana como restrição "must_not".
A lista de filtros é coletada do template de pesquisa customizada
do periódico, quanto este template existir.
"""
custom_queries = set([utils.cleanup_string(i) for i in journal_titles.load(issn).get('must_not', [])])
for item in custom_queries:
query = {
"match": {
"reference_source_cleaned": item
}
}
yield query
def has_optmized_journal_queries(self, issn):
if journal_titles.load(issn):
return True
return False
